{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:55:33Z","timestamp":1773658533342,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s13042-025-02939-9","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:41:03Z","timestamp":1769697663000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Thyroid nodule segmentation in medical images via an improved UNet model"],"prefix":"10.1007","volume":"17","author":[{"given":"Mubina","family":"Zaka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Hamad","family":"Shirazi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assad","family":"Rasheed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atef","family":"Masmoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"issue":"5","key":"2939_CR1","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1007\/s00034-021-01916-1","volume":"41","author":"A Giorgio","year":"2022","unstructured":"Giorgio A, Guaragnella C, Rizzi M (2022) An effective CAD system for heart sound abnormality detection. Circuits Syst Signal Process 41(5):2845\u20132870","journal-title":"Circuits Syst Signal Process"},{"issue":"3","key":"2939_CR2","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/diagnostics11030393","volume":"11","author":"M Mansourian","year":"2021","unstructured":"Mansourian M, Khademi S, Marateb HR (2021) A comprehensive review of computer-aided diagnosis of major mental and neurological disorders and suicide: a biostatistical perspective on data mining. Diagnostics 11(3):393","journal-title":"Diagnostics"},{"issue":"3","key":"2939_CR3","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ejrad.2012.03.005","volume":"82","author":"C Dromain","year":"2013","unstructured":"Dromain C, Boyer B, Ferre R, Canale S, Delaloge S, Balleyguier C (2013) Computed-aided diagnosis (CAD) in the detection of breast cancer. Eur J Radiol 82(3):417\u2013423","journal-title":"Eur J Radiol"},{"issue":"10","key":"2939_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0283568","volume":"18","author":"A Rasheed","year":"2023","unstructured":"Rasheed A, Shirazi SH, Umar AI, Shahzad M, Yousaf W, Khan Z (2023) Cervical cell\u2019s nucleus segmentation through an improved UNet architecture. PLoS One 18(10):e0283568","journal-title":"PLoS One"},{"issue":"12","key":"2939_CR5","doi-asserted-by":"publisher","first-page":"5768","DOI":"10.3390\/app12125768","volume":"12","author":"Z Khan","year":"2022","unstructured":"Khan Z, Umar AI, Shirazi SH, Rasheed A, Yousaf W, Assam M, Mohamed A (2022) Lung\u2019s segmentation using context-aware regressive conditional GAN. Appl Sci 12(12):5768","journal-title":"Appl Sci"},{"key":"2939_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105807","volume":"147","author":"A Rasheed","year":"2022","unstructured":"Rasheed A, Umar AI, Shirazi SH, Khan Z, Nawaz S, Shahzad M (2022) Automatic eczema classification in clinical images based on hybrid deep neural network. Comput Biol Med 147:105807","journal-title":"Comput Biol Med"},{"key":"2939_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2020.00102","volume":"11","author":"A Prete","year":"2020","unstructured":"Prete A, de Borges Souza P, Censi S, Muzza M, Nucci N, Sponziello M (2020) Update on fundamental mechanisms of thyroid cancer. Front Endocrinol 11:102","journal-title":"Front Endocrinol"},{"key":"2939_CR8","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1007\/s00371-018-1519-5","volume":"34","author":"L Bi","year":"2018","unstructured":"Bi L, Feng D, Kim J (2018) Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. Vis Comput 34:1043\u20131052","journal-title":"Vis Comput"},{"key":"2939_CR9","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18 (pp. 234\u2013241). Springer International Publishing","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2939_CR10","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022), October Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision (pp. 205\u2013218). Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"2939_CR11","doi-asserted-by":"crossref","unstructured":"Yan X, Tang H, Sun S, Ma H, Kong D, Xie X (2022) After-unet: Axial fusion transformer unet for medical image segmentation. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision (pp. 3971\u20133981)","DOI":"10.1109\/WACV51458.2022.00333"},{"issue":"1","key":"2939_CR12","doi-asserted-by":"publisher","first-page":"17843","DOI":"10.1038\/s41598-019-54434-1","volume":"9","author":"VY Park","year":"2019","unstructured":"Park VY, Han K, Seong YK, Park MH, Kim EK, Moon HJ, Kwak JY (2019) Diagnosis of thyroid nodules: performance of a deep learning convolutional neural network model vs. radiologists. Sci Rep 9(1):17843","journal-title":"Sci Rep"},{"key":"2939_CR13","doi-asserted-by":"crossref","unstructured":"Ying X, Yu Z, Yu R, Li X, Yu M, Zhao M, Liu K (2018) Thyroid nodule segmentation in ultrasound images based on cascaded convolutional neural network. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13\u201316, 2018, Proceedings, Part VI 25 (pp. 373\u2013384). Springer International Publishing","DOI":"10.1007\/978-3-030-04224-0_32"},{"key":"2939_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103805","volume":"77","author":"Q Yang","year":"2022","unstructured":"Yang Q, Geng C, Chen R, Pang C, Han R, Lyu L, Zhang Y (2022) DMU-Net: dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. Biomed Signal Process Control 77:103805","journal-title":"Biomed Signal Process Control"},{"key":"2939_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107186","volume":"227","author":"X Lin","year":"2022","unstructured":"Lin X, Zhou X, Tong T, Nie X, Wang L, Zheng H, Gao Q (2022) A super-resolution guided network for improving automated thyroid nodule segmentation. Comput Methods Programs Biomed 227:107186","journal-title":"Comput Methods Programs Biomed"},{"key":"2939_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104636","volume":"83","author":"G Li","year":"2023","unstructured":"Li G, Chen R, Zhang J, Liu K, Geng C, Lyu L (2023) Fusing enhanced Transformer and large kernel CNN for malignant thyroid nodule segmentation. Biomed Signal Process Control 83:104636","journal-title":"Biomed Signal Process Control"},{"key":"2939_CR17","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8087624","author":"P Poudel","year":"2018","unstructured":"Poudel P, Illanes A, Sheet D, Friebe M (2018) Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches. J Healthc Eng. https:\/\/doi.org\/10.1155\/2018\/8087624","journal-title":"J Healthc Eng"},{"issue":"4","key":"2939_CR18","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1109\/TMI.2007.907328","volume":"27","author":"EN Kollorz","year":"2008","unstructured":"Kollorz EN, Hahn DA, Linke R, Goecke TW, Hornegger J, Kuwert T (2008) Quantification of thyroid volume using 3-d ultrasound imaging. IEEE Trans Med Imaging 27(4):457\u2013466","journal-title":"IEEE Trans Med Imaging"},{"key":"2939_CR19","doi-asserted-by":"publisher","DOI":"10.5120\/7959-0924","author":"J Kaur","year":"2012","unstructured":"Kaur J, Jindal A (2012) Comparison of thyroid segmentation algorithms in ultrasound and scintigraphy images. Int J Comput Appl. https:\/\/doi.org\/10.5120\/7959-0924","journal-title":"Int J Comput Appl"},{"issue":"4","key":"2939_CR20","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/TITB.2008.2007192","volume":"13","author":"MA Savelonas","year":"2008","unstructured":"Savelonas MA, Iakovidis DK, Legakis I, Maroulis D (2008) Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images. IEEE Trans Inf Technol Biomed 13(4):519\u2013527","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"2939_CR21","doi-asserted-by":"crossref","unstructured":"Garg H, Jindal A (2013), July Segmentation of thyroid gland in ultrasound image using neural network. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1\u20135). IEEE","DOI":"10.1109\/ICCCNT.2013.6726797"},{"issue":"19","key":"2939_CR22","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.3390\/math10193484","volume":"10","author":"DT Nguyen","year":"2022","unstructured":"Nguyen DT, Choi J, Park KR (2022) Thyroid nodule segmentation in ultrasound image based on information fusion of suggestion and enhancement networks. Mathematics 10(19):3484","journal-title":"Mathematics"},{"issue":"3","key":"2939_CR23","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/JBHI.2018.2852718","volume":"23","author":"W Song","year":"2018","unstructured":"Song W, Li S, Liu J, Qin H, Zhang B, Zhang S, Hao A (2018) Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Health Inf 23(3):1215\u20131224","journal-title":"IEEE J Biomed Health Inf"},{"key":"2939_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104431","volume":"81","author":"W Cui","year":"2023","unstructured":"Cui W, Meng D, Lu K, Wu Y, Pan Z, Li X, Sun S (2023) Automatic segmentation of ultrasound images using SegNet and local Nakagami distribution fitting model. Biomed Signal Process Control 81:104431","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"2939_CR25","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1016\/j.ultrasmedbio.2022.09.010","volume":"49","author":"A Kunapinun","year":"2023","unstructured":"Kunapinun A, Dailey MN, Songsaeng D, Parnichkun M, Keatmanee C, Ekpanyapong M (2023) Improving GAN learning dynamics for thyroid nodule segmentation. Ultrasound Med Biol 49(2):416\u2013430","journal-title":"Ultrasound Med Biol"},{"key":"2939_CR26","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.ultras.2016.09.011","volume":"73","author":"J Ma","year":"2017","unstructured":"Ma J, Wu F, Zhu J, Xu D, Kong D (2017) A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 73:221\u2013230","journal-title":"Ultrasonics"},{"key":"2939_CR27","doi-asserted-by":"crossref","unstructured":"Xie S, Yu J, Liu T, Chang Q, Niu L, Sun W (2019), June Thyroid nodule detection in ultrasound images with convolutional neural networks. In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1442\u20131446). IEEE","DOI":"10.1109\/ICIEA.2019.8834375"},{"key":"2939_CR28","doi-asserted-by":"crossref","unstructured":"Ajilisa OA, Jagathyraj VP, Sabu MK (2020, July) Computer-aided diagnosis of thyroid nodule from ultrasound images using transfer learning from deep convolutional neural network models. 2020 advanced computing and communication technologies for high performance applications (ACCTHPA). IEEE, pp 237\u2013241","DOI":"10.1109\/ACCTHPA49271.2020.9213210"},{"issue":"1","key":"2939_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56395-x","volume":"9","author":"E Lee","year":"2019","unstructured":"Lee E, Ha H, Kim HJ, Moon HJ, Byon JH, Huh S, Son J, Yoon J, Han K, Kwak JY (2019) Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks. Sci Rep 9(1):19854","journal-title":"Sci Rep"},{"issue":"3","key":"2939_CR30","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1121\/10.0001924","volume":"148","author":"J Shao","year":"2020","unstructured":"Shao J, Zheng J, Zhang B (2020) Deep convolutional neural networks for thyroid tumor grading using ultrasound b-mode images. J Acoust Soc Am 148(3):1529\u20131535","journal-title":"J Acoust Soc Am"},{"issue":"1","key":"2939_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0262128","volume":"17","author":"X Zhang","year":"2022","unstructured":"Zhang X, Lee VC, Rong J, Liu F, Kong H (2022) Multi-channel convolutional neural network architectures for thyroid cancer detection. PLoS One 17(1):e0262128","journal-title":"PLoS One"},{"key":"2939_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106164","volume":"150","author":"Z Xiang","year":"2022","unstructured":"Xiang Z, Zhuo Q, Zhao C, Deng X, Zhu T, Wang T, Jiang W, Lei B (2022) Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis. Comput Biol Med 150:106164","journal-title":"Comput Biol Med"},{"key":"2939_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106823","volume":"220","author":"Xinyu Zhang","year":"2022","unstructured":"Zhang Xinyu, Lee Vincent CS, Rong Jia, Lee James C., Liu Feng (2022) Deep convolutional neural networks in thyroid disease detection: a multi-classification comparison by ultrasonography and computed tomography. Comput Methods Programs Biomed 220:106823","journal-title":"Comput Methods Programs Biomed"},{"issue":"8","key":"2939_CR34","doi-asserted-by":"publisher","first-page":"10563","DOI":"10.1007\/s13369-023-07674-3","volume":"48","author":"Noura Aboudi","year":"2023","unstructured":"Aboudi Noura, Khachnaoui Hajer, Moussa Olfa, Khlifa Nawres (2023) Bilinear pooling for thyroid nodule classification in ultrasound imaging. Arab J Sci Eng 48(8):10563\u201310573","journal-title":"Arab J Sci Eng"},{"key":"2939_CR35","doi-asserted-by":"crossref","unstructured":"Rahman M, Mostafijur M, Munir (2024) and Radu Marculescu. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11769\u201311779","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"2939_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Qing Xu","year":"2023","unstructured":"Xu Qing, Ma Zhicheng, Duan Wenting (2023) DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation. Comput Biol Med 154:106626","journal-title":"Comput Biol Med"},{"key":"2939_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109524","volume":"139","author":"Yongheng Sun","year":"2023","unstructured":"Sun Yongheng, Dai Duwei, Zhang Qianni, Wang Yaqi, Xu Songhua, Lian Chunfeng (2023) MSCA-Net: multi-scale contextual attention network for skin lesion segmentation. Pattern Recognition 139:109524","journal-title":"Pattern Recognition"},{"key":"2939_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103241","volume":"97","author":"Duwei Dai","year":"2024","unstructured":"Dai Duwei, Dong Caixia, Yan Qingsen, Sun Yongheng, Zhang Chunyan, Li Zongfang, Xu Songhua (2024) I2u-net: a dual-path u-net with rich information interaction for medical image segmentation. Med Image Anal 97:103241","journal-title":"Med Image Anal"},{"key":"2939_CR39","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"2939_CR40","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, and Serge Belongie (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer visionpattern recognition, pp. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"2939_CR41","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"issue":"3","key":"2939_CR42","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1002\/mp.16672","volume":"51","author":"H Dai","year":"2024","unstructured":"Dai H, Xie W, Xia E (2024) SK-Unet++: an improved Unet\u2009+\u2009+\u2009network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images. Med Phys 51(3):1798\u20131811","journal-title":"Med Phys"},{"key":"2939_CR43","doi-asserted-by":"crossref","unstructured":"Misra D (2019) Mish: A self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681","DOI":"10.5244\/C.34.191"},{"key":"2939_CR44","doi-asserted-by":"crossref","unstructured":"Yu F, Wang D, Shelhamer E, Darrell T (2018) Deep layer aggregation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2403\u20132412","DOI":"10.1109\/CVPR.2018.00255"},{"issue":"24","key":"2939_CR45","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abc6f2","volume":"65","author":"Bailin Yang","year":"2020","unstructured":"Yang Bailin, Yan Meiying, Yan Zaoming, Zhu Changrui, Xu Dong, Dong Fangfang (2020) Segmentation and classification of thyroid follicular neoplasm using cascaded convolutional neural network. Phys Med Biol 65(24):245040","journal-title":"Phys Med Biol"},{"key":"2939_CR46","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.872601","volume":"16","author":"Xingqing Nie","year":"2022","unstructured":"Nie Xingqing, Zhou Xiaogen, Tong Tong, Lin Xingtao, Wang Luoyan, Zheng Haonan, Li Jing et al (2022) N-Net: a novel dense fully convolutional neural network for thyroid nodule segmentation. Front Neurosci 16:872601","journal-title":"Front Neurosci"},{"key":"2939_CR47","doi-asserted-by":"crossref","unstructured":"Pedraza L, Vargas C, Narv\u00e1ez Fabi\u00e1n, Dur\u00e1n O, Mu\u00f1oz E, Romero E (2015) An open access thyroid ultrasound image database. In 10th International symposium on medical information processing and analysis, vol. 9287, pp. 188\u2013193. SPIE","DOI":"10.1117\/12.2073532"},{"key":"2939_CR48","doi-asserted-by":"crossref","unstructured":"Abraham N, Naimul Mefraz Khan (2019) A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp. 683\u2013687. IEEE","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"2939_CR49","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"6","key":"2939_CR50","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Zongwei Zhou","year":"2019","unstructured":"Zhou Zongwei, Siddiquee Md Mahfuzur Rahman, Tajbakhsh Nima, Liang Jianming (2019) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"2939_CR51","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pp. 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2939_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101989","volume":"70","author":"Cheng Xue","year":"2021","unstructured":"Xue Cheng, Zhu Lei, Fu Huazhu, Hu Xiaowei, Li Xiaomeng, Zhang Hai, Heng Pheng-Ann (2021) Global guidance network for breast lesion segmentation in ultrasound images. Med Image Anal 70:101989","journal-title":"Med Image Anal"},{"key":"2939_CR53","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"Nabil Ibtehaz","year":"2020","unstructured":"Ibtehaz Nabil, Rahman M. Sohel (2020) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks 121:74\u201387","journal-title":"Neural Networks"},{"key":"2939_CR54","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, and Yuyin Zhou (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"key":"2939_CR55","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pp. 205\u2013218. Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"2939_CR56","doi-asserted-by":"crossref","unstructured":"Hou Q, Cheng M-M, Borji XHA, Tu Z, Philip HST (2017) Deeply supervised salient object detection with short connections. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3203\u20133212","DOI":"10.1109\/CVPR.2017.563"},{"key":"2939_CR57","doi-asserted-by":"crossref","unstructured":"Deng Z, Zhu XHL, Xu X, Qin J, Han G, Pheng-Ann Heng (2018) R3net: Recurrent residual refinement network for saliency detection. In Proceedings of the 27th international joint conference on artificial intelligence, vol. 684690. Menlo Park, CA, USA: AAAI Press","DOI":"10.24963\/ijcai.2018\/95"},{"key":"2939_CR58","unstructured":"Kingma DP (2014) and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"2939_CR59","unstructured":"Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747"},{"issue":"5","key":"2939_CR60","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TFUZZ.2019.2958559","volume":"28","author":"D Wu","year":"2019","unstructured":"Wu D, Yuan Y, Huang J, Tan Y (2019) Optimize TSK fuzzy systems for regression problems: minibatch gradient descent with regularization, DropRule, and adabound (MBGD-RDA). IEEE Trans Fuzzy Syst 28(5):1003\u20131015","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2939_CR61","doi-asserted-by":"crossref","unstructured":"Armato III, Samuel G, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Binsheng, Zhao et al (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical Physics. 38, no. 2 : 915\u2013931","DOI":"10.1118\/1.3528204"},{"issue":"4","key":"2939_CR62","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MoiHoon Yap","year":"2017","unstructured":"Yap Moi Hoon, Pons Gerard, Marti Joan, Ganau Sergi, Sentis Melcior, Zwiggelaar Reyer, Davison Adrian K., Marti Robert (2017) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218\u20131226","journal-title":"IEEE J Biomed Health Inform"},{"key":"2939_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111371","author":"Aimei Dong","year":"2025","unstructured":"Dong Aimei, Liu Jian, Lv Guohua, Cheng Jinyong (2025) GLMR-Net: Global-to-local mutually reinforcing network for pneumonia segmentation and classification. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2025.111371","journal-title":"Pattern Recognit"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02939-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02939-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02939-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T09:56:39Z","timestamp":1773654999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02939-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":63,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2939"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02939-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"16 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"43"}}