{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:09:16Z","timestamp":1777043356260,"version":"3.51.4"},"reference-count":181,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972351"],"award-info":[{"award-number":["61972351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62111530300"],"award-info":[{"award-number":["62111530300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project for Basic Business Expenses of Zhejiang Provincial Colleges and Universities","award":["JRK22003"],"award-info":[{"award-number":["JRK22003"]}]},{"name":"Zhejiang Engineering Research Center of Intelligent Medicine","award":["2016E10011"],"award-info":[{"award-number":["2016E10011"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-023-09214-4","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:16Z","timestamp":1701252136000},"page":"22239-22254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Survey on deep learning in multimodal medical imaging for cancer detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Yan","family":"Tian","sequence":"first","affiliation":[]},{"given":"Zhaocheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yujun","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Ruili","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhihong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Linyang","family":"He","sequence":"additional","affiliation":[]},{"given":"Xuran","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"9214_CR1","doi-asserted-by":"crossref","unstructured":"Cai D, Sun X, Zhou N, Han X, Yao J (2019) Efficient mitosis detection in breast cancer histology images by rcnn. In: International symposium on biomedical imaging, pp\u00a0919\u2013922","DOI":"10.1109\/ISBI.2019.8759461"},{"key":"9214_CR2","doi-asserted-by":"crossref","unstructured":"Fatima M, Khan MA, Shaheen S, Almujally NA, Wang S-H (2023) B2c3netf2: breast cancer classification using an end-to-end deep learning feature fusion and satin bowerbird optimization controlled newton raphson feature selection. In: CAAI transactions on intelligence technology","DOI":"10.1049\/cit2.12219"},{"issue":"6","key":"9214_CR3","doi-asserted-by":"publisher","first-page":"10404","DOI":"10.3934\/mbe.2023457","volume":"20","author":"S Chaudhury","year":"2023","unstructured":"Chaudhury S, Sau K, Khan MA, Shabaz M (2023) Deep transfer learning for idc breast cancer detection using fast ai technique and sqeezenet architecture. Math Biosci Eng 20(6):10404\u201310427","journal-title":"Math Biosci Eng"},{"issue":"4","key":"9214_CR4","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.dsp.2006.10.008","volume":"17","author":"K Polat","year":"2007","unstructured":"Polat K, G\u00fcne\u015f S (2007) Breast cancer diagnosis using least square support vector machine. Digital Signal Process 17(4):694\u2013701","journal-title":"Digital Signal Process"},{"issue":"2","key":"9214_CR5","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","volume":"36","author":"MF Akay","year":"2009","unstructured":"Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240\u20133247","journal-title":"Expert Syst Appl"},{"issue":"2","key":"9214_CR6","doi-asserted-by":"publisher","first-page":"835","DOI":"10.7314\/APJCP.2016.17.2.835","volume":"17","author":"MD Podolsky","year":"2016","unstructured":"Podolsky MD, Barchuk AA, Kuznetcov VI, Gusarova NF, Gaidukov VS, Tarakanov SA (2016) Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels. Asian Pac J Cancer Prevent 17(2):835\u2013838","journal-title":"Asian Pac J Cancer Prevent"},{"issue":"7","key":"9214_CR7","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.3390\/diagnostics13071285","volume":"13","author":"S Malik","year":"2023","unstructured":"Malik S, Akram T, Awais M, Khan MA, Hadjouni M, Elmannai H, Alasiry A, Marzougui M, Tariq U (2023) An improved skin lesion boundary estimation for enhanced-intensity images using hybrid metaheuristics. Diagnostics 13(7):1285","journal-title":"Diagnostics"},{"issue":"7","key":"9214_CR8","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.3390\/diagnostics13071238","volume":"13","author":"K Jabeen","year":"2023","unstructured":"Jabeen K, Khan MA, Balili J, Alhaisoni M, Almujally NA, Alrashidi H, Tariq U, Cha J-H (2023) Bc2netrf: breast cancer classification from mammogram images using enhanced deep learning features and equilibrium-jaya controlled regula falsi-based features selection. Diagnostics 13(7):1238","journal-title":"Diagnostics"},{"key":"9214_CR9","doi-asserted-by":"publisher","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R (2019) A survey of deep learning-based object detection. IEEE Access 7:128837\u2013128868","journal-title":"IEEE Access"},{"key":"9214_CR10","doi-asserted-by":"crossref","unstructured":"Rani S, Singh BK, Koundal D, Athavale VA (2022) Localization of stroke lesion in mri images using object detection techniques: a comprehensive review. In: Neuroscience informatics, p\u00a0100070","DOI":"10.1016\/j.neuri.2022.100070"},{"key":"9214_CR11","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","volume":"83","author":"Z Hu","year":"2018","unstructured":"Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q (2018) Deep learning for image-based cancer detection and diagnosis\u2014a survey. Pattern Recogn 83:134\u2013149","journal-title":"Pattern Recogn"},{"issue":"3","key":"9214_CR12","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TCBB.2013.84","volume":"10","author":"S Rathore","year":"2013","unstructured":"Rathore S, Hussain M, Ali A, Khan A (2013) A recent survey on colon cancer detection techniques. IEEE\/ACM Trans Comput Biol Bioinf 10(3):545\u2013563","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"9214_CR13","doi-asserted-by":"crossref","unstructured":"Baumgartner M, J\u00e4ger PF, Isensee F, Maier-Hein KH (2021) Nndetection: a self-configuring method for medical object detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0530\u2013539","DOI":"10.1007\/978-3-030-87240-3_51"},{"issue":"9","key":"9214_CR14","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1016\/j.jiph.2020.06.033","volume":"13","author":"T Saba","year":"2020","unstructured":"Saba T (2020) Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J Infect Public Health 13(9):1274\u20131289","journal-title":"J Infect Public Health"},{"key":"9214_CR15","doi-asserted-by":"crossref","unstructured":"Khan MA, Akram T, Zhang Y-D, Alhaisoni M, Al\u00a0Hejaili A, Shaban KA, Tariq U, Zayyan MH (2023) Skinnet-endo: multiclass skin lesion recognition using deep neural network and entropy-normal distribution optimization algorithm with elm. Int J Imaging Syst Technol","DOI":"10.1002\/ima.22863"},{"key":"9214_CR16","doi-asserted-by":"crossref","unstructured":"Painuli D, Bhardwaj S, et al. (2022) Recent advancement in cancer diagnosis using machine learning and deep learning techniques: a comprehensive review. Comput Biol Med 105580","DOI":"10.1016\/j.compbiomed.2022.105580"},{"issue":"1","key":"9214_CR17","doi-asserted-by":"publisher","first-page":"49","DOI":"10.33545\/26633582.2022.v4.i1a.68","volume":"4","author":"VR Allugunti","year":"2022","unstructured":"Allugunti VR (2022) Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. Int J Eng Comput Sci 4(1):49\u201356","journal-title":"Int J Eng Comput Sci"},{"issue":"6","key":"9214_CR18","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.3390\/ijerph19063211","volume":"19","author":"A Rasool","year":"2022","unstructured":"Rasool A, Bunterngchit C, Tiejian L, Islam MR, Qu Q, Jiang Q (2022) Improved machine learning-based predictive models for breast cancer diagnosis. Int J Environ Res Public Health 19(6):3211","journal-title":"Int J Environ Res Public Health"},{"key":"9214_CR19","volume":"20","author":"NN Prakash","year":"2023","unstructured":"Prakash NN, Rajesh V, Namakhwa DL, Pande SD, Ahammad SH (2023) A densenet cnn-based liver lesion prediction and classification for future medical diagnosis. Sci Afr 20:e01629","journal-title":"Sci Afr"},{"key":"9214_CR20","doi-asserted-by":"crossref","unstructured":"Jiang H, Diao Z, Shi T, Zhou Y, Wang F, Hu W, Zhu X, Luo S, Tong G, Yao Y-D (2023) A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput Biol Med 106726","DOI":"10.1016\/j.compbiomed.2023.106726"},{"key":"9214_CR21","doi-asserted-by":"crossref","unstructured":"Anter AM, Abualigah L (2023) Deep federated machine learning-based optimization methods for liver tumor diagnosis: a review. Archiv Comput Methods Eng 1\u201320","DOI":"10.1007\/s11831-023-09901-4"},{"key":"9214_CR22","doi-asserted-by":"crossref","unstructured":"Zhang W, Zhu L, Hallinan J, Zhang S, Makmur A, Cai Q, Ooi BC (2022) Boostmis: boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a020666\u201320676","DOI":"10.1109\/CVPR52688.2022.02001"},{"key":"9214_CR23","doi-asserted-by":"crossref","unstructured":"Zhang S, Li Z, Zhou H-Y, Ma J, Yu Y (2023) Advancing 3d medical image analysis with variable dimension transform based supervised 3d pre-training. Neurocomputing","DOI":"10.1016\/j.neucom.2023.01.012"},{"key":"9214_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.106530","volume":"81","author":"B Guan","year":"2020","unstructured":"Guan B, Zhang G, Yao J, Wang X, Wang M (2020) Arm fracture detection in x-rays based on improved deep convolutional neural network. Comput Electr Eng 81:106530","journal-title":"Comput Electr Eng"},{"issue":"9","key":"9214_CR25","doi-asserted-by":"publisher","first-page":"2428","DOI":"10.1109\/TMI.2021.3077913","volume":"40","author":"G Zhao","year":"2021","unstructured":"Zhao G, Fang C, Li G, Jiao L, Yu Y (2021) Contralaterally enhanced networks for thoracic disease detection. IEEE Trans Med Imaging 40(9):2428\u20132438","journal-title":"IEEE Trans Med Imaging"},{"key":"9214_CR26","doi-asserted-by":"crossref","unstructured":"Xie C, Cao S, Wei D, Zhou H, Ma K, Zhang X, Qian B, Wang L, Zheng Y (2021) Recist-net: lesion detection via grouping keypoints on recist-based annotation. In: International symposium on biomedical imaging, pp\u00a0921\u2013924","DOI":"10.1109\/ISBI48211.2021.9433794"},{"key":"9214_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103774","volume":"121","author":"R Agarwal","year":"2020","unstructured":"Agarwal R, D\u00edaz O, Yap MH, Llad\u00f3 X, Mart\u00ed R (2020) Deep learning for mass detection in full field digital mammograms. Comput Biol Med 121:103774","journal-title":"Comput Biol Med"},{"issue":"7","key":"9214_CR28","first-page":"2006","volume":"24","author":"H Cao","year":"2020","unstructured":"Cao H, Liu H, Song E, Ma G, Xu X, Jin R, Liu T, Hung C-C (2020) A two-stage convolutional neural networks for lung nodule detection. IEEE J Biomed Health Inform 24(7):2006\u20132015","journal-title":"IEEE J Biomed Health Inform"},{"key":"9214_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105128","volume":"189","author":"X Xu","year":"2020","unstructured":"Xu X, Wang C, Guo J, Yang L, Bai H, Li W, Yi Z (2020) Deepln: a framework for automatic lung nodule detection using multi-resolution ct screening images. Knowl Based Syst 189:105128","journal-title":"Knowl Based Syst"},{"key":"9214_CR30","first-page":"11093","volume":"34","author":"C Jiang","year":"2020","unstructured":"Jiang C, Wang S, Liang X, Xu H, Xiao N (2020) Elixirnet: relation-aware network architecture adaptation for medical lesion detection. Proc AAAI Conf Artif Intell 34:11093\u201311100","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"9214_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102345","volume":"77","author":"S Wang","year":"2022","unstructured":"Wang S, Zhu Y, Lee S, Elton DC, Shen TC, Tang Y, Peng Y, Lu Z, Summers RM (2022) Global-local attention network with multi-task uncertainty loss for abnormal lymph node detection in mr images. Med Image Anal 77:102345","journal-title":"Med Image Anal"},{"key":"9214_CR32","doi-asserted-by":"crossref","unstructured":"Tang Y-B, Yan K, Tang Y-X, Liu J, Xiao J, Summers RM (2019) Uldor: a universal lesion detector for ct scans with pseudo masks and hard negative example mining. In: International symposium on biomedical imaging, pp\u00a0833\u2013836","DOI":"10.1109\/ISBI.2019.8759478"},{"key":"9214_CR33","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.media.2019.01.013","volume":"53","author":"C Li","year":"2019","unstructured":"Li C, Wang X, Liu W, Latecki LJ, Wang B, Huang J (2019) weakly supervised mitosis detection in breast histopathology images using concentric loss. Med Image Anal 53:165\u2013178","journal-title":"Med Image Anal"},{"key":"9214_CR34","doi-asserted-by":"crossref","unstructured":"Sun Y, Huang X, Molina EGL, Dong L, Zhang Q (2020) Signet ring cells detection in histology images with similarity learning. In: International symposium on biomedical imaging, pp\u00a0490\u2013494","DOI":"10.1109\/ISBI45749.2020.9098547"},{"key":"9214_CR35","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a0779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"9214_CR36","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, pp\u00a021\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9214_CR37","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, vol\u00a028"},{"key":"9214_CR38","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a0658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"9214_CR39","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a03431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"8","key":"9214_CR40","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.1109\/TMI.2019.2891305","volume":"38","author":"H Lin","year":"2019","unstructured":"Lin H, Chen H, Graham S, Dou Q, Rajpoot N, Heng P-A (2019) Fast scannet: fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection. IEEE Trans Med Imaging 38(8):1948\u20131958","journal-title":"IEEE Trans Med Imaging"},{"key":"9214_CR41","doi-asserted-by":"crossref","unstructured":"Tao Q, Ge Z, Cai J, Yin J, See S (2019) Improving deep lesion detection using 3d contextual and spatial attention. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0185\u2013193","DOI":"10.1007\/978-3-030-32226-7_21"},{"key":"9214_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108006","volume":"118","author":"J Wu","year":"2021","unstructured":"Wu J, Xu H, Zhang S, Li X, Chen J, Zheng J, Gao Y, Tian Y, Liang Y, Ji R (2021) Joint segmentation and detection of covid-19 via a sequential region generation network. Pattern Recogn 118:108006","journal-title":"Pattern Recogn"},{"key":"9214_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101861","volume":"67","author":"D Zhang","year":"2021","unstructured":"Zhang D, Chen B, Li S (2021) Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy. Med Image Anal 67:101861","journal-title":"Med Image Anal"},{"key":"9214_CR44","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a0770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9214_CR45","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patcog.2018.07.031","volume":"85","author":"H Xie","year":"2019","unstructured":"Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019) Automated pulmonary nodule detection in ct images using deep convolutional neural networks. Pattern Recogn 85:109\u2013119","journal-title":"Pattern Recogn"},{"issue":"4","key":"9214_CR46","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","volume":"89","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Wang Q-C, Xu M-D, Zhang Z, Cheng J, Zhong Y-S, Zhang Y-Q, Chen W-F, Yao L-Q, Zhou P-H et al (2019) Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 89(4):806\u2013815","journal-title":"Gastrointest Endosc"},{"key":"9214_CR47","doi-asserted-by":"crossref","unstructured":"Li B, Li Y, Eliceiri KW (2021) Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a014318\u201314328","DOI":"10.1109\/CVPR46437.2021.01409"},{"issue":"2","key":"9214_CR48","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/s41591-020-01174-9","volume":"27","author":"W Lotter","year":"2021","unstructured":"Lotter W, Diab AR, Haslam B, Kim JG, Grisot G, Wu E, Wu K, Onieva JO, Boyer Y, Boxerman JL et al (2021) Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med 27(2):244\u2013249","journal-title":"Nat Med"},{"key":"9214_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105823","volume":"200","author":"GH Aly","year":"2021","unstructured":"Aly GH, Marey M, El-Sayed SA, Tolba MF (2021) Yolo based breast masses detection and classification in full-field digital mammograms. Comput Methods Programs Biomed 200:105823","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"9214_CR50","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214133","volume":"14","author":"X Zhang","year":"2019","unstructured":"Zhang X, Chen F, Yu T, An J, Huang Z, Liu J, Hu W, Wang L, Duan H, Si J (2019) Real-time gastric polyp detection using convolutional neural networks. PLoS ONE 14(3):e0214133","journal-title":"PLoS ONE"},{"key":"9214_CR51","doi-asserted-by":"crossref","unstructured":"Zlocha M, Dou Q, Glocker B (2019) Improving retinanet for ct lesion detection with dense masks from weak recist labels. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0402\u2013410","DOI":"10.1007\/978-3-030-32226-7_45"},{"key":"9214_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102142","volume":"72","author":"Y Sun","year":"2021","unstructured":"Sun Y, Huang X, Zhou H, Zhang Q (2021) Srpn: similarity-based region proposal networks for nuclei and cells detection in histology images. Med Image Anal 72:102142","journal-title":"Med Image Anal"},{"key":"9214_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108568","volume":"120","author":"Y Qiu","year":"2022","unstructured":"Qiu Y, Wang R, Hou F, Singh S, Ma Z, Jia X (2022) Adversarial multi-task learning with inverse mapping for speech enhancement. Appl Soft Comput 120:108568","journal-title":"Appl Soft Comput"},{"key":"9214_CR54","doi-asserted-by":"crossref","unstructured":"Han Z, Huang H, Fan Q, Li Y, Li Y, Chen X (2022) Smd-yolo: an efficient and lightweight detection method for mask wearing status during the covid-19 pandemic. In: Computer methods and programs in biomedicine, p\u00a0106888","DOI":"10.1016\/j.cmpb.2022.106888"},{"key":"9214_CR55","doi-asserted-by":"publisher","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf R, Afzal S, Rehman AU, Gul S, Baber J, Bakhtyar M, Mehmood I, Song O-Y, Maqsood M (2020) Region-of-interest based transfer learning assisted framework for skin cancer detection. IEEE Access 8:147858\u2013147871","journal-title":"IEEE Access"},{"issue":"5","key":"9214_CR56","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, Zhu X, Wang R (2017) Efficient knn classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774\u20131785","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9214_CR57","doi-asserted-by":"crossref","unstructured":"Bazai SU, Jang-Jaccard J, Wang R (2017) Anonymizing k-nn classification on mapreduce. In: International conference on mobile networks and management, pp\u00a0364\u2013377","DOI":"10.1007\/978-3-319-90775-8_29"},{"key":"9214_CR58","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neucom.2019.04.028","volume":"350","author":"Z Li","year":"2019","unstructured":"Li Z, Dong M, Wen S, Hu X, Zhou P, Zeng Z (2019) Clu-cnns: object detection for medical images. Neurocomputing 350:53\u201359","journal-title":"Neurocomputing"},{"key":"9214_CR59","doi-asserted-by":"crossref","unstructured":"Yang S, Zhou X, Wang J, Xie G, Lv C, Gao P, Lv B (2020) Unsupervised domain adaptation for cross-device oct lesion detection via learning adaptive features. In: International symposium on biomedical imaging, pp\u00a01570\u20131573","DOI":"10.1109\/ISBI45749.2020.9098380"},{"key":"9214_CR60","doi-asserted-by":"crossref","unstructured":"Figueira G, Wang Y, Sun L, Zhou H, Zhang Q (2020) \u201cAdversarial-based domain adaptation networks for unsupervised tumour detection in histopathology. In: International symposium on biomedical imaging, pp\u00a01284\u20131288","DOI":"10.1109\/ISBI45749.2020.9098699"},{"key":"9214_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105518","volume":"195","author":"Y Li","year":"2020","unstructured":"Li Y, Zhang L, Chen H, Cheng L (2020) Mass detection in mammograms by bilateral analysis using convolution neural network. Comput Methods Programs Biomed 195:105518","journal-title":"Comput Methods Programs Biomed"},{"key":"9214_CR62","doi-asserted-by":"crossref","unstructured":"Zhang L, Li Y, Chen H, Cheng L (2019) Mammographic mass detection by bilateral analysis based on convolution neural network. In: International conference on image processing, pp\u00a0784\u2013788","DOI":"10.1109\/ICIP.2019.8803761"},{"key":"9214_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102117","volume":"72","author":"H-Y Zhou","year":"2021","unstructured":"Zhou H-Y, Wang C, Li H, Wang G, Zhang S, Li W, Yu Y (2021) Ssmd: semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation. Med Image Anal 72:102117","journal-title":"Med Image Anal"},{"key":"9214_CR64","doi-asserted-by":"crossref","unstructured":"Li H, Han X, Kang Y, Shi X, Yan M, Tong Z, Bu Q, Cui L, Feng J, Yang L (2020) A novel loss calibration strategy for object detection networks training on sparsely annotated pathological datasets. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0320\u2013329","DOI":"10.1007\/978-3-030-59722-1_31"},{"key":"9214_CR65","doi-asserted-by":"crossref","unstructured":"Wang Y, Lu L, Cheng C-T, Jin D, Harrison AP, Xiao J, Liao C-H, Miao S (2019) Weakly supervised universal fracture detection in pelvic x-rays. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0459\u2013467","DOI":"10.1007\/978-3-030-32226-7_51"},{"key":"9214_CR66","doi-asserted-by":"crossref","unstructured":"Yang H-H, Wang F-E, Sun C, Huang K-C, Chen H-W, Chen Y, Chen H-C, Liao C-Y, Kao S-H, Wang Y-CF, et\u00a0al (2021) Leveraging auxiliary information from emr for weakly supervised pulmonary nodule detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0251\u2013261","DOI":"10.1007\/978-3-030-87234-2_24"},{"key":"9214_CR67","doi-asserted-by":"crossref","unstructured":"Bijoy M, Akondi SM, Abdul\u00a0Fathaah S, Raut A, Pournami P, Jayaraj P (2022) Cervix type detection using a self-supervision boosted object detection technique. Int J Imaging Syst Technol","DOI":"10.1002\/ima.22696"},{"key":"9214_CR68","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/LSP.2021.3089912","volume":"28","author":"D Zhou","year":"2021","unstructured":"Zhou D, Tian Y, Chen W-G, Huang G (2021) Self-supervised saliency estimation for pixel embedding in road detection. IEEE Signal Process Lett 28:1325\u20131329","journal-title":"IEEE Signal Process Lett"},{"key":"9214_CR69","doi-asserted-by":"crossref","unstructured":"Qiu Y, Wang R, Singh S, Ma Z, Hou F (2021) Self-supervised learning based phone-fortified speech enhancement","DOI":"10.21437\/Interspeech.2021-734"},{"key":"9214_CR70","doi-asserted-by":"crossref","unstructured":"Li Z, Cui Z, Wang S, Qi Y, Ouyang X, Chen Q, Yang Y, Xue Z, Shen D, Cheng J-Z (2021) Domain generalization for mammography detection via multi-style and multi-view contrastive learning. In: International conference on medical image computing and computer-assisted intervention, pp\u00a098\u2013108","DOI":"10.1007\/978-3-030-87234-2_10"},{"key":"9214_CR71","doi-asserted-by":"crossref","unstructured":"Shou Y, Meng T, Ai W, Xie C, Liu H, Wang Y (2022) Object detection in medical images based on hierarchical transformer and mask mechanism. Comput Intell Neurosci 2022","DOI":"10.1155\/2022\/5863782"},{"key":"9214_CR72","unstructured":"Cranmer M, Sanchez\u00a0Gonzalez A, Battaglia P, Xu R, Cranmer K, Spergel D, Ho S (2020) Discovering symbolic models from deep learning with inductive biases. In: Advances in neural information processing systems, vol\u00a033, pp\u00a017429\u201317442"},{"key":"9214_CR73","doi-asserted-by":"crossref","unstructured":"Mao Y, Wang N, Zhou W, Li H (2021) Joint inductive and transductive learning for video object segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a09670\u20139679","DOI":"10.1109\/ICCV48922.2021.00953"},{"key":"9214_CR74","doi-asserted-by":"crossref","unstructured":"Wang D, Zhang Y, Zhang K, Wang L (2020) Focalmix: semi-supervised learning for 3d medical image detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a03951\u20133960","DOI":"10.1109\/CVPR42600.2020.00401"},{"key":"9214_CR75","doi-asserted-by":"crossref","unstructured":"Liu J, Yao J, Bagheri M, Sandfort V, Summers RM (2019) A semi-supervised cnn learning method with pseudo-class labels for atherosclerotic vascular calcification detection. In: International symposium on biomedical imaging, pp\u00a0780\u2013783","DOI":"10.1109\/ISBI.2019.8759189"},{"key":"9214_CR76","doi-asserted-by":"crossref","unstructured":"Li J, Yang S, Huang X, Da Q, Yang X, Hu Z, Duan Q, Wang C, Li H (2019) Signet ring cell detection with a semi-supervised learning framework. In: International conference on information processing in medical imaging, pp\u00a0842\u2013854","DOI":"10.1007\/978-3-030-20351-1_66"},{"key":"9214_CR77","doi-asserted-by":"crossref","unstructured":"Zhao Z, Pang F, Liu Z, Ye C (2021) Positive-unlabeled learning for cell detection in histopathology images with incomplete annotations. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0509\u2013518","DOI":"10.1007\/978-3-030-87237-3_49"},{"key":"9214_CR78","unstructured":"Settles B (2009) Active learning literature survey"},{"key":"9214_CR79","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107158","volume":"100","author":"Y Tian","year":"2020","unstructured":"Tian Y, Cheng G, Gelernter J, Yu S, Song C, Yang B (2020) Joint temporal context exploitation and active learning for video segmentation. Pattern Recogn 100:107158","journal-title":"Pattern Recogn"},{"key":"9214_CR80","doi-asserted-by":"crossref","unstructured":"Liu J, Cao L, Tian Y (2020) Deep active learning for effective pulmonary nodule detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0609\u2013618","DOI":"10.1007\/978-3-030-59725-2_59"},{"key":"9214_CR81","doi-asserted-by":"crossref","unstructured":"Guo Z, Zhang R, Li Q, Liu X, Nemoto D, Togashi K, Niroshana SI, Shi Y, Zhu X (2020) Reduce false-positive rate by active learning for automatic polyp detection in colonoscopy videos. In: International symposium on biomedical imaging, pp\u00a01655\u20131658","DOI":"10.1109\/ISBI45749.2020.9098500"},{"issue":"4","key":"9214_CR82","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/JBHI.2021.3094311","volume":"26","author":"R Elakkiya","year":"2021","unstructured":"Elakkiya R, Subramaniyaswamy V, Vijayakumar V, Mahanti A (2021) Cervical cancer diagnostics healthcare system using hybrid object detection adversarial networks. IEEE J Biomed Health Inform 26(4):1464\u20131471","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"9214_CR83","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11042-021-10627-3","volume":"81","author":"R Elakkiya","year":"2022","unstructured":"Elakkiya R, Teja KSS, Jegatha Deborah L, Bisogni C, Medaglia C (2022) Imaging based cervical cancer diagnostics using small object detection-generative adversarial networks. Multimedia Tools Appl 81(1):191\u2013207","journal-title":"Multimedia Tools Appl"},{"key":"9214_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102154","volume":"73","author":"J Zhao","year":"2021","unstructured":"Zhao J, Li D, Xiao X, Accorsi F, Marshall H, Cossetto T, Kim D, McCarthy D, Dawson C, Knezevic S et al (2021) United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast mri. Med Image Anal 73:102154","journal-title":"Med Image Anal"},{"key":"9214_CR85","doi-asserted-by":"crossref","unstructured":"Bisla D, Choromanska A, Berman RS, Stein JA, Polsky D (2019) Towards automated melanoma detection with deep learning: data purification and augmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2019.00330"},{"key":"9214_CR86","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2019.07.094","volume":"366","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali P, Zareapoor M, Wang R, Jain DK, Yang J (2019) G-ganisr: gradual generative adversarial network for image super resolution. Neurocomputing 366:140\u2013153","journal-title":"Neurocomputing"},{"key":"9214_CR87","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.inffus.2021.02.014","volume":"72","author":"P Shamsolmoali","year":"2021","unstructured":"Shamsolmoali P, Zareapoor M, Granger E, Zhou H, Wang R, Celebi ME, Yang J (2021) Image synthesis with adversarial networks: a comprehensive survey and case studies. Inf Fusion 72:126\u2013146","journal-title":"Inf Fusion"},{"key":"9214_CR88","doi-asserted-by":"crossref","unstructured":"Liu L, Muelly M, Deng J, Pfister T, Li L-J (2019) Generative modeling for small-data object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a06073\u20136081","DOI":"10.1109\/ICCV.2019.00617"},{"key":"9214_CR89","doi-asserted-by":"crossref","unstructured":"Han C, Kitamura Y, Kudo A, Ichinose A, Rundo L, Furukawa Y, Umemoto K, Li Y, Nakayama H (2019) Synthesizing diverse lung nodules wherever massively: 3d multi-conditional gan-based ct image augmentation for object detection. In: International conference on 3D vision, pp\u00a0729\u2013737","DOI":"10.1109\/3DV.2019.00085"},{"key":"9214_CR90","doi-asserted-by":"crossref","unstructured":"Hammami M, Friboulet D, K\u00e9chichian R (2020) Cycle gan-based data augmentation for multi-organ detection in ct images via yolo. In: International conference on image processing, pp\u00a0390\u2013393","DOI":"10.1109\/ICIP40778.2020.9191127"},{"key":"9214_CR91","doi-asserted-by":"crossref","unstructured":"Wang Z, Dong N, Rosario SD, Xu M, Xie P, Xing EP (2019) Ellipse detection of optic discand-cup boundary in fundus images. In: International symposium on biomedical imaging, pp\u00a0601\u2013604","DOI":"10.1109\/ISBI.2019.8759173"},{"key":"9214_CR92","doi-asserted-by":"crossref","unstructured":"Han Y, Chen C, Tewfik A, Ding Y, Peng Y (2021) Pneumonia detection on chest x-ray using radiomic features and contrastive learning. In: International symposium on biomedical imaging, pp\u00a0247\u2013251","DOI":"10.1109\/ISBI48211.2021.9433853"},{"key":"9214_CR93","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol\u00a030"},{"issue":"12","key":"9214_CR94","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.1109\/TITS.2018.2886283","volume":"20","author":"Yan Tian","year":"2019","unstructured":"Tian Yan, Gelernter Judith, Wang Xun, Li Jianyuan, Yu Yizhou (2019) Traffic sign detection using a multi-scale recurrent attention network. IEEE Trans Intell Transport Syst 20(12):4466\u20134475. https:\/\/doi.org\/10.1109\/TITS.2018.2886283","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"9214_CR95","doi-asserted-by":"crossref","unstructured":"Li S, Zhang Z, Lu Y (2020) Efficient detection of emvi in rectal cancer via richer context information and feature fusion. In: International symposium on biomedical imaging, pp\u00a01464\u20131468","DOI":"10.1109\/ISBI45749.2020.9098677"},{"key":"9214_CR96","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102197","volume":"73","author":"L Cao","year":"2021","unstructured":"Cao L, Yang J, Rong Z, Li L, Xia B, You C, Lou G, Jiang L, Du C, Meng H et al (2021) A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Med Image Anal 73:102197","journal-title":"Med Image Anal"},{"key":"9214_CR97","doi-asserted-by":"crossref","unstructured":"Shao Q, Gong L, Ma K, Liu H, Zheng Y (2019) Attentive ct lesion detection using deep pyramid inference with multi-scale booster. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0301\u2013309","DOI":"10.1007\/978-3-030-32226-7_34"},{"key":"9214_CR98","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2020.07.078","volume":"417","author":"Y Tian","year":"2020","unstructured":"Tian Y, Zhang Y, Zhou D, Cheng G, Chen W-G, Wang R (2020) Triple attention network for video segmentation. Neurocomputing 417:202\u2013211","journal-title":"Neurocomputing"},{"key":"9214_CR99","doi-asserted-by":"crossref","unstructured":"Yu X, Lou B, Zhang D, Winkel D, Arrahmane N, Diallo M, Meng T, Busch Hv, Grimm R, Kiefer B, et\u00a0al (2020) Deep attentive panoptic model for prostate cancer detection using biparametric mri scans. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0594\u2013604","DOI":"10.1007\/978-3-030-59719-1_58"},{"key":"9214_CR100","doi-asserted-by":"crossref","unstructured":"Shahroudnejad A, Qin X, Balachandran S, Dehghan M, Zonoobi D, Jaremko J, Kapur J, Jagersand M, Noga M, Punithakumar K (2021) Tun-det: a novel network for thyroid ultrasound nodule detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0656\u2013667","DOI":"10.1007\/978-3-030-87193-2_62"},{"key":"9214_CR101","doi-asserted-by":"crossref","unstructured":"Zhang Z, Li S, Wang Z, Lu Y (2020) A novel and efficient tumor detection framework for pancreatic cancer via ct images. In: International conference of the IEEE engineering in medicine and biology society, pp\u00a01160\u20131164","DOI":"10.1109\/EMBC44109.2020.9176172"},{"issue":"1","key":"9214_CR102","doi-asserted-by":"publisher","first-page":"13","DOI":"10.32604\/csse.2023.034172","volume":"46","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Khan MA, Zhu Z, Wang S (2023) Snelm: squeezenet-guided elm for covid-19 recognition. Comput Syst Sci Eng 46(1):13\u201326","journal-title":"Comput Syst Sci Eng"},{"issue":"12","key":"9214_CR103","doi-asserted-by":"publisher","first-page":"3507","DOI":"10.1109\/TMI.2021.3089547","volume":"40","author":"Y Liu","year":"2021","unstructured":"Liu Y, Ji S (2021) Cleftnet: augmented deep learning for synaptic cleft detection from brain electron microscopy. IEEE Trans Med Imaging 40(12):3507\u20133518","journal-title":"IEEE Trans Med Imaging"},{"key":"9214_CR104","doi-asserted-by":"crossref","unstructured":"Luo L, Chen H, Zhou Y, Lin H, Heng P-A (2021) Oxnet: deep omni-supervised thoracic disease detection from chest x-rays. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0537\u2013548","DOI":"10.1007\/978-3-030-87196-3_50"},{"key":"9214_CR105","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang Y, Liu L, Zhong C, Tian J, Zhang Y, Shi Z, He Z (2020) Fgb: feature guidance branch for organ detection in medical images. In: International symposium on biomedical imaging, pp\u00a0349\u2013353","DOI":"10.1109\/ISBI45749.2020.9098499"},{"key":"9214_CR106","doi-asserted-by":"crossref","unstructured":"Ma J, Li X, Li H, Wang R, Menze B, Zheng W-S (2021) Cross-view relation networks for mammogram mass detection. In: International conference on pattern recognition, pp\u00a08632\u20138638","DOI":"10.1109\/ICPR48806.2021.9413132"},{"key":"9214_CR107","doi-asserted-by":"crossref","unstructured":"Li Z, Zhang S, Zhang J, Huang K, Wang Y, Yu Y (2019) Mvp-net: multi-view fpn with position-aware attention for deep universal lesion detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a013\u201321","DOI":"10.1007\/978-3-030-32226-7_2"},{"issue":"6","key":"9214_CR108","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1049\/iet-cvi.2018.5492","volume":"13","author":"Y Tian","year":"2019","unstructured":"Tian Y, Cao Y, Wu J, Hu W, Song C, Yang T (2019) Multi-cue combination network for action-based video classification. IET Comput Vis 13(6):542\u2013548","journal-title":"IET Comput Vis"},{"key":"9214_CR109","doi-asserted-by":"crossref","unstructured":"Ren Y, Lu J, Liang Z, Grimm LJ, Kim C, Taylor-Cho M, Yoon S, Marks JR, Lo JY (2021)Retina-match: ipsilateral mammography lesion matching in a single shot detection pipeline. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0345\u2013354","DOI":"10.1007\/978-3-030-87240-3_33"},{"key":"9214_CR110","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhou Z, Zhang S, Luo L, Zhang Q, Zhang F, Li X, Wang Y, Yu Y (2019) From unilateral to bilateral learning: Detecting mammogram masses with contrasted bilateral network. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0477\u2013485","DOI":"10.1007\/978-3-030-32226-7_53"},{"key":"9214_CR111","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang F, Zhang Q, Wang S, Wang Y Yu Y (2020) Cross-view correspondence reasoning based on bipartite graph convolutional network for mammogram mass detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a03812\u20133822","DOI":"10.1109\/CVPR42600.2020.00387"},{"key":"9214_CR112","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang Y, Wang J, Zhou X, He Y, Zhang T (2021) Ellipsenet: anchor-free ellipse detection for automatic cardiac biometrics in fetal echocardiography. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0218\u2013227","DOI":"10.1007\/978-3-030-87234-2_21"},{"key":"9214_CR113","doi-asserted-by":"crossref","unstructured":"Gr\u00e4bel P, \u00d6zkan \u00d6, Crysandt M, Herwartz R, Baumann M, Klinkhammer BM, Boor P, Br\u00fcmmendorf TH, Merhof D (2020) Circular anchors for the detection of hematopoietic cells using retinanet. In: International symposium on biomedical imaging, pp\u00a0249\u2013253","DOI":"10.1109\/ISBI45749.2020.9098398"},{"issue":"3","key":"9214_CR114","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1109\/TMI.2021.3122835","volume":"41","author":"EH Nguyen","year":"2021","unstructured":"Nguyen EH, Yang H, Deng R, Lu Y, Zhu Z, Roland JT, Lu L, Landman BA, Fogo AB, Huo Y (2021) Circle representation for medical object detection. IEEE Trans Med Imaging 41(3):746\u2013754","journal-title":"IEEE Trans Med Imaging"},{"key":"9214_CR115","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102287","volume":"75","author":"X Luo","year":"2022","unstructured":"Luo X, Song T, Wang G, Chen J, Chen Y, Li K, Metaxas DN, Zhang S (2022) Scpm-net: an anchor-free 3d lung nodule detection network using sphere representation and center points matching. Med Image Anal 75:102287","journal-title":"Med Image Anal"},{"key":"9214_CR116","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106260","volume":"208","author":"H Hu","year":"2021","unstructured":"Hu H, Liu A, Zhou Q, Guan Q, Li X, Chen Q (2021) An adaptive learning method of anchor shape priors for biological cells detection and segmentation. Comput Methods Programs Biomed 208:106260","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"9214_CR117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3504033","volume":"18","author":"Y Tian","year":"2022","unstructured":"Tian Y, Zhang Y, Chen W-G, Liu D, Wang H, Xu H, Han J, Ge Y (2022) 3d tooth instance segmentation learning objectness and affinity in point cloud. ACM Trans Multimed Comput Commun Appl 18(4):1\u201316","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"9214_CR118","doi-asserted-by":"publisher","first-page":"175135","DOI":"10.1109\/ACCESS.2019.2953517","volume":"7","author":"K Xia","year":"2019","unstructured":"Xia K, Yin H (2019) Liver detection algorithm based on an improved deep network combined with edge perception. IEEE Access 7:175135\u2013175142","journal-title":"IEEE Access"},{"key":"9214_CR119","doi-asserted-by":"crossref","unstructured":"Ouyang X, Che J, Chen Q, Li Z, Zhan Y, Xue Z, Wang Q, Cheng J-Z, Shen D (2021) Self-adversarial learning for detection of clustered microcalcifications in mammograms. In: International conference on medical image computing and computer-assisted intervention, pp\u00a078\u201387","DOI":"10.1007\/978-3-030-87234-2_8"},{"key":"9214_CR120","doi-asserted-by":"crossref","unstructured":"Xiao L, Zhu C, Liu J, Luo C, Liu P, Zhao Y (2019) \u201cLearning from suspected target: Bootstrapping performance for breast cancer detection in mammography,\u201d in International Conference on Medical Image Computing and Computer-assisted Intervention, pp.\u00a0468\u2013476","DOI":"10.1007\/978-3-030-32226-7_52"},{"key":"9214_CR121","doi-asserted-by":"crossref","unstructured":"Yu J, Chen C, Yang X, Wang Y, Yan D, Zhang J, Ni D (2020) Computer-aided tumor diagnosis in automated breast ultrasound using 3d detection network. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0181\u2013189","DOI":"10.1007\/978-3-030-59725-2_18"},{"key":"9214_CR122","doi-asserted-by":"crossref","unstructured":"Huang D, Cheng J, Fan R, Su Z, Ma Q Li J (2021) Bone marrow cell recognition: training deep object detection with a new loss function. In: International conference on imaging systems and techniques, pp\u00a01\u20136","DOI":"10.1109\/IST50367.2021.9651340"},{"issue":"7","key":"9214_CR123","first-page":"F0268","volume":"63","author":"J Willoughby","year":"2022","unstructured":"Willoughby J, Sallo F, Zouache M, Cilkova M, Dubis A, Lilaonitkul W (2022) Object detection on medical images with the aid of contrastive gated attention. Investig Ophthalmol Visual Sci 63(7):F0268-2998","journal-title":"Investig Ophthalmol Visual Sci"},{"issue":"3","key":"9214_CR124","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300","journal-title":"Neural Process Lett"},{"key":"9214_CR125","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106172","volume":"207","author":"K-B Chen","year":"2021","unstructured":"Chen K-B, Xuan Y, Lin A-J, Guo S-H (2021) Esophageal cancer detection based on classification of gastrointestinal ct images using improved faster rcnn. Comput Methods Prog Biomed 207:106172","journal-title":"Comput Methods Prog Biomed"},{"key":"9214_CR126","unstructured":"Harsono IW, Liawatimena S, Cenggoro TW (2020) Lung nodule detection and classification from thorax ct-scan using retinanet with transfer learning. In: Journal of King Saud University-computer and information sciences"},{"key":"9214_CR127","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103735","volume":"120","author":"A Bria","year":"2020","unstructured":"Bria A, Marrocco C, Tortorella F (2020) Addressing class imbalance in deep learning for small lesion detection on medical images. Comput Biol Med 120:103735","journal-title":"Comput Biol Med"},{"key":"9214_CR128","doi-asserted-by":"crossref","unstructured":"Lin Y, Su J, Wang X, Li X, Liu J, Cheng K-T, Yang X (2019) Automated pulmonary embolism detection from ctpa images using an end-to-end convolutional neural network. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0280\u2013288","DOI":"10.1007\/978-3-030-32251-9_31"},{"key":"9214_CR129","doi-asserted-by":"crossref","unstructured":"Li Y, Fan Y (2020) Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection. In: 2020 IEEE 17th international symposium on biomedical imaging, pp\u00a01866\u20131869","DOI":"10.1109\/ISBI45749.2020.9098317"},{"key":"9214_CR130","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102019","volume":"70","author":"T Wollmann","year":"2021","unstructured":"Wollmann T, Rohr K (2021) Deep consensus network: aggregating predictions to improve object detection in microscopy images. Med Image Anal 70:102019","journal-title":"Med Image Anal"},{"key":"9214_CR131","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ins.2018.12.047","volume":"483","author":"Z Chen","year":"2019","unstructured":"Chen Z, Wang R, Zhang Z, Wang H, Xu L (2019) Background-foreground interaction for moving object detection in dynamic scenes. Inf Sci 483:65\u201381","journal-title":"Inf Sci"},{"key":"9214_CR132","doi-asserted-by":"crossref","unstructured":"Lin C, Wu H, Wen Z, Qin J (2021) Automated malaria cells detection from blood smears under severe class imbalance via importance-aware balanced group softmax. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0455\u2013465","DOI":"10.1007\/978-3-030-87237-3_44"},{"key":"9214_CR133","doi-asserted-by":"crossref","unstructured":"Li H, Han H, Zhou SK (2020) Bounding maps for universal lesion detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0417\u2013428","DOI":"10.1007\/978-3-030-59719-1_41"},{"key":"9214_CR134","doi-asserted-by":"crossref","unstructured":"Li H, Chen L, Han H, Chi Y, Zhou SK (2021) \u201cConditional training with bounding map for universal lesion detection,\u201d in International Conference on Medical Image Computing and Computer-assisted Intervention, pp.\u00a0141\u2013152","DOI":"10.1007\/978-3-030-87240-3_14"},{"key":"9214_CR135","doi-asserted-by":"crossref","unstructured":"Liu J, Cao L, Akin O, Tian Y (2019) 3dfpn-hs$$^{2}$$: 3d feature pyramid network based high sensitivity and specificity pulmonary nodule detection. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0513\u2013521","DOI":"10.1007\/978-3-030-32226-7_57"},{"key":"9214_CR136","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2019.01.104","volume":"347","author":"Y Tian","year":"2019","unstructured":"Tian Y, Hu W, Jiang H, Wu J (2019) Densely connected attentional pyramid residual network for human pose estimation. Neurocomputing 347:13\u201323","journal-title":"Neurocomputing"},{"key":"9214_CR137","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1613\/jair.1.11338","volume":"64","author":"Y Tian","year":"2019","unstructured":"Tian Y, Wang X, Wu J, Wang R, Yang B (2019) Multi-scale hierarchical residual network for dense captioning. J Artifi Intell Res 64:181\u2013196","journal-title":"J Artifi Intell Res"},{"key":"9214_CR138","doi-asserted-by":"crossref","unstructured":"Zhang F, Luo L, Sun X, Zhou Z, Li X, Yu Y, Wang Y (2019) Cascaded generative and discriminative learning for microcalcification detection in breast mammograms. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a012578\u201312586","DOI":"10.1109\/CVPR.2019.01286"},{"key":"9214_CR139","doi-asserted-by":"crossref","unstructured":"Dass JMA, Kumar SM (2022) A novel approach for small object detection in medical images through deep ensemble convolution neural network. Int J Adv Comput Sci Appl 13(3)","DOI":"10.14569\/IJACSA.2022.0130380"},{"key":"9214_CR140","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101897","volume":"68","author":"HA Qadir","year":"2021","unstructured":"Qadir HA, Shin Y, Solhusvik J, Bergsland J, Aabakken L, Balasingham I (2021) Toward real-time polyp detection using fully cnns for 2d gaussian shapes prediction. Med Image Anal 68:101897","journal-title":"Med Image Anal"},{"key":"9214_CR141","doi-asserted-by":"crossref","unstructured":"Yu X, Lou B, Shi B, Winkel D, Arrahmane N, Diallo M, Meng T, von Busch H, Grimm R, Kiefer B, et\u00a0al (2020) False positive reduction using multiscale contextual features for prostate cancer detection in multi-parametric mri scans. In: International symposium on biomedical imaging, pp\u00a01355\u20131359","DOI":"10.1109\/ISBI45749.2020.9098338"},{"issue":"5","key":"9214_CR142","doi-asserted-by":"publisher","first-page":"4555","DOI":"10.1016\/j.aej.2021.03.005","volume":"60","author":"L Xue","year":"2021","unstructured":"Xue L, Yan W, Luo P, Zhang X, Chaikovska T, Liu K, Gao W, Yang K (2021) Detection and localization of hand fractures based on ga_faster r-cnn. Alex Eng J 60(5):4555\u20134562","journal-title":"Alex Eng J"},{"key":"9214_CR143","doi-asserted-by":"crossref","unstructured":"Li F, Huang H, Wu Y, Cai C, Huang Y, Ding X (2019) Lung nodule detection with a 3d convnet via iou self-normalization and maxout unit. In: International conference on acoustics, speech and signal processing, pp\u00a01214\u20131218","DOI":"10.1109\/ICASSP.2019.8683537"},{"key":"9214_CR144","first-page":"2893","volume":"36","author":"Q Xu","year":"2022","unstructured":"Xu Q, Zhong Y, Neumann U (2022) Behind the curtain: learning occluded shapes for 3d object detection. Proc AAAI Conf Artif Intell 36:2893\u20132901","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"2","key":"9214_CR145","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3390\/diagnostics13020248","volume":"13","author":"M Nawaz","year":"2023","unstructured":"Nawaz M, Nazir T, Baili J, Khan MA, Kim YJ, Cha J-H (2023) Cxray-effdet: chest disease detection and classification from x-ray images using the efficientdet model. Diagnostics 13(2):248","journal-title":"Diagnostics"},{"issue":"3","key":"9214_CR146","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/s40846-018-0415-9","volume":"39","author":"J Chakraborty","year":"2019","unstructured":"Chakraborty J, Midya A, Mukhopadhyay S, Rangayyan RM, Sadhu A, Singla V, Khandelwal N (2019) Computer-aided detection of mammographic masses using hybrid region growing controlled by multilevel thresholding. J Medical Biol Eng 39(3):352\u2013366","journal-title":"J Medical Biol Eng"},{"key":"9214_CR147","doi-asserted-by":"crossref","unstructured":"Wang A, Sun Y, Kortylewski A, Yuille AL (2020) Robust object detection under occlusion with context-aware compositionalnets. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a012645\u201312654","DOI":"10.1109\/CVPR42600.2020.01266"},{"issue":"12","key":"9214_CR148","doi-asserted-by":"publisher","first-page":"3920","DOI":"10.1109\/TMI.2020.3007642","volume":"39","author":"L Xiao","year":"2020","unstructured":"Xiao L, Luo C, Yu T, Luo Y, Wang M, Yu F, Li Y, Tian C, Qiao J (2020) Deepacev2: automated chromosome enumeration in metaphase cell images using deep convolutional neural networks. IEEE Trans Med Imaging 39(12):3920\u20133932","journal-title":"IEEE Trans Med Imaging"},{"key":"9214_CR149","unstructured":"Jaeger PF, Kohl SA, Bickelhaupt S, Isensee F, Kuder TA, Schlemmer H-P, Maier-Hein KH (2020) Retina u-net: embarrassingly simple exploitation of segmentation supervision for medical object detection. In: Machine learning for health workshop, pp\u00a0171\u2013183"},{"key":"9214_CR150","doi-asserted-by":"crossref","unstructured":"Yan K, Tang Y, Peng Y, Sandfort V, Bagheri M, Lu Z, Summers RM (2019) Mulan: multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0194\u2013202","DOI":"10.1007\/978-3-030-32226-7_22"},{"key":"9214_CR151","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla R, Robu M, Luengo I, Stoyanov D (2021) Scalable joint detection and segmentation of surgical instruments with weak supervision. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0501\u2013511","DOI":"10.1007\/978-3-030-87196-3_47"},{"key":"9214_CR152","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102415","volume":"78","author":"X Zhang","year":"2022","unstructured":"Zhang X, Zhu X, Tang K, Zhao Y, Lu Z, Feng Q (2022) Ddtnet: a dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer. Med Image Anal 78:102415","journal-title":"Med Image Anal"},{"key":"9214_CR153","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.102780","volume":"56","author":"X Zhao","year":"2020","unstructured":"Zhao X, Xie P, Wang M, Li W, Pickhardt PJ, Xia W, Xiong F, Zhang R, Xie Y, Jian J et al (2020) Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: a multicentre study. EBioMedicine 56:102780","journal-title":"EBioMedicine"},{"key":"9214_CR154","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105674","volume":"197","author":"D Yu","year":"2020","unstructured":"Yu D, Zhang K, Huang L, Zhao B, Zhang X, Guo X, Li M, Gu Z, Fu G, Hu M et al (2020) Detection of peripherally inserted central catheter (picc) in chest x-ray images: a multi-task deep learning model. Comput Methods Prog Biomed 197:105674","journal-title":"Comput Methods Prog Biomed"},{"key":"9214_CR155","doi-asserted-by":"crossref","unstructured":"Chen Y, Chen J, Xiao B, Wu Z, Chi Y, Xie X, Hua X (2019) Volume r-cnn: unified framework for ct object detection and instance segmentation. In: International symposium on biomedical imaging, pp\u00a0872\u2013876","DOI":"10.1109\/ISBI.2019.8759390"},{"key":"9214_CR156","doi-asserted-by":"crossref","unstructured":"Kale A, Jawade I, Kakade P, Jadhav R, Kulkarni N (2022) Pairnet: a deep learning-based object detection and segmentation system. In: Machine intelligence and smart systems, pp\u00a0423\u2013436","DOI":"10.1007\/978-981-16-9650-3_33"},{"key":"9214_CR157","doi-asserted-by":"crossref","unstructured":"Boot T, Irshad H (2020) Diagnostic assessment of deep learning algorithms for detection and segmentation of lesion in mammographic images. In: International conference on medical image computing and computer-assisted intervention, pp\u00a056\u201365","DOI":"10.1007\/978-3-030-59719-1_6"},{"key":"9214_CR158","doi-asserted-by":"crossref","unstructured":"Zhang W, Li G, Wang F, Yu Y, Lin L, Liang H, et\u00a0al. (2019) Simultaneous lung field detection and segmentation for pediatric chest radiographs. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0594\u2013602","DOI":"10.1007\/978-3-030-32226-7_66"},{"key":"9214_CR159","doi-asserted-by":"crossref","unstructured":"Feng X, Duan L, Chen J (2021) An automated method with anchor-free detection and u-shaped segmentation for nuclei instance segmentation. In: Proceedings of the ACM international conference on multimedia in Asia, pp\u00a01\u20136","DOI":"10.1145\/3444685.3446258"},{"key":"9214_CR160","doi-asserted-by":"crossref","unstructured":"Le N, Yamazaki K, Quach KG, Truong D, Savvides M (2021) A multi-task contextual atrous residual network for brain tumor detection and segmentation. In: International conference on pattern recognition, pp\u00a05943\u20135950","DOI":"10.1109\/ICPR48806.2021.9412414"},{"issue":"6","key":"9214_CR161","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1007\/s11554-020-00961-4","volume":"17","author":"G Cheng","year":"2020","unstructured":"Cheng G, Cheng J, Luo M, He L, Tian Y, Wang R (2020) Effective and efficient multitask learning for brain tumor segmentation. J Real-Time Image Proc 17(6):1951\u20131960","journal-title":"J Real-Time Image Proc"},{"key":"9214_CR162","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2017.01.098","volume":"253","author":"Y Tian","year":"2017","unstructured":"Tian Y, Wang H, Wang X (2017) Object localization via evaluation multi-task learning. Neurocomputing 253:34\u201341","journal-title":"Neurocomputing"},{"key":"9214_CR163","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ins.2020.04.041","volume":"533","author":"H Zheng","year":"2020","unstructured":"Zheng H, Wang R, Ji W, Zong M, Wong WK, Lai Z, Lv H (2020) Discriminative deep multi-task learning for facial expression recognition. Inf Sci 533:60\u201371","journal-title":"Inf Sci"},{"issue":"3","key":"9214_CR164","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s10846-013-9889-4","volume":"76","author":"T Chen","year":"2014","unstructured":"Chen T, Dai B, Wang R, Liu D (2014) Gaussian-process-based real-time ground segmentation for autonomous land vehicles. J Intell Robot Syst 76(3):563\u2013582","journal-title":"J Intell Robot Syst"},{"key":"9214_CR165","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105584","volume":"196","author":"MA Al-Antari","year":"2020","unstructured":"Al-Antari MA, Han S-M, Kim T-S (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital x-ray mammograms. Comput Methods Prog Biomed 196:105584","journal-title":"Comput Methods Prog Biomed"},{"key":"9214_CR166","doi-asserted-by":"crossref","unstructured":"Sui D, Zhang K, Liu W, Chen J, Ma X, Tian Z (2021) Cst: a multitask learning framework for colorectal cancer region mining based on transformer. BioMed Res Int 2021","DOI":"10.1155\/2021\/6207964"},{"key":"9214_CR167","doi-asserted-by":"crossref","unstructured":"Sainz\u00a0de Cea MV, Diedrich K, Bakalo R, Ness L, Richmond D (2020) Multi-task learning for detection and classification of cancer in screening mammography. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0241\u2013250","DOI":"10.1007\/978-3-030-59725-2_24"},{"key":"9214_CR168","doi-asserted-by":"crossref","unstructured":"Liu Y, Wu Y-H, Ban Y, Wang H, Cheng M-M (2020) Rethinking computer-aided tuberculosis diagnosis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a02646\u20132655","DOI":"10.1109\/CVPR42600.2020.00272"},{"key":"9214_CR169","doi-asserted-by":"crossref","unstructured":"Tang H, Zhang C, Xie X (2019) Nodulenet: decoupled false positive reduction for pulmonary nodule detection and segmentation. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0266\u2013274","DOI":"10.1007\/978-3-030-32226-7_30"},{"key":"9214_CR170","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106061","volume":"204","author":"Y Liang","year":"2021","unstructured":"Liang Y, Pan C, Sun W, Liu Q, Du Y (2021) Global context-aware cervical cell detection with soft scale anchor matching. Comput Methods Prog Biomed 204:106061","journal-title":"Comput Methods Prog Biomed"},{"key":"9214_CR171","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105360","volume":"190","author":"WK Moon","year":"2020","unstructured":"Moon WK, Huang Y-S, Hsu C-H, Chien T-YC, Chang JM, Lee SH, Huang C-S, Chang R-F (2020) Computer-aided tumor detection in automated breast ultrasound using a 3-d convolutional neural network. Comput Methods Prog Biomed 190:105360","journal-title":"Comput Methods Prog Biomed"},{"key":"9214_CR172","doi-asserted-by":"crossref","unstructured":"Capia ER, Sousa AM, Falc\u00e3o AX (2020) Improving lung nodule detection with learnable non-maximum suppression. In: International symposium on biomedical imaging, pp\u00a01861\u20131865","DOI":"10.1109\/ISBI45749.2020.9098573"},{"key":"9214_CR173","doi-asserted-by":"crossref","unstructured":"Tang H, Liu X, Xie X (2019) An end-to-end framework for integrated pulmonary nodule detection and false positive reduction. In: 2019 IEEE 16th international symposium on biomedical imaging, pp\u00a0859\u2013862","DOI":"10.1109\/ISBI.2019.8759244"},{"key":"9214_CR174","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102377","volume":"77","author":"R Su","year":"2022","unstructured":"Su R, van der Sluijs M, Cornelissen SA, Lycklama G, Hofmeijer J, Majoie CB, van Doormaal PJ, van Es AC, Ruijters D, Niessen WJ et al (2022) Spatio-temporal deep learning for automatic detection of intracranial vessel perforation in digital subtraction angiography during endovascular thrombectomy. Med Image Anal 77:102377","journal-title":"Med Image Anal"},{"key":"9214_CR175","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang Y, Zhang J, Mu X (2019) Comparison of multiple feature extractors on faster rcnn for breast tumor detection. In: International symposium on next generation electronics, pp\u00a01\u20134","DOI":"10.1109\/ISNE.2019.8896490"},{"issue":"4","key":"9214_CR176","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s10115-021-01549-5","volume":"63","author":"F Hou","year":"2021","unstructured":"Hou F, Wang R, Zhou Y (2021) Transfer learning for fine-grained entity typing. Knowl Inf Syst 63(4):845\u2013866","journal-title":"Knowl Inf Syst"},{"key":"9214_CR177","doi-asserted-by":"crossref","unstructured":"Wang X, Cai Z, Gao D, Vasconcelos N (2019) Towards universal object detection by domain attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp\u00a07289\u20137298","DOI":"10.1109\/CVPR.2019.00746"},{"key":"9214_CR178","doi-asserted-by":"crossref","unstructured":"Ajmal M, Khan MA, Akram T, Alqahtani A, Alhaisoni M, Armghan A, Althubiti SA, Alenezi F (2022) Bf2sknet: best deep learning features fusion-assisted framework for multiclass skin lesion classification. Neural Comput Appl 1\u201317","DOI":"10.1007\/s00521-022-08084-6"},{"key":"9214_CR179","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102052","volume":"71","author":"X Liu","year":"2021","unstructured":"Liu X, Guo X, Liu Y, Yuan Y (2021) Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images. Med Image Anal 71:102052","journal-title":"Med Image Anal"},{"key":"9214_CR180","doi-asserted-by":"crossref","unstructured":"Cho H, Nishimura K, Watanabe K, Bise R (2021) Cell detection in domain shift problem using pseudo-cell-position heatmap. In: International conference on medical image computing and computer-assisted intervention, pp\u00a0384\u2013394","DOI":"10.1007\/978-3-030-87237-3_37"},{"key":"9214_CR181","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp\u00a02980\u20132988","DOI":"10.1109\/ICCV.2017.324"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09214-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09214-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09214-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T21:20:04Z","timestamp":1757107204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09214-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,29]]},"references-count":181,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["9214"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09214-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,29]]},"assertion":[{"value":"5 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}