{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T01:20:45Z","timestamp":1773364845950,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-023-08729-0","type":"journal-article","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T16:47:26Z","timestamp":1687538846000},"page":"19187-19220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images"],"prefix":"10.1007","volume":"35","author":[{"given":"Ajay","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pramod Kumar","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"issue":"11","key":"8729_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11):1\u201313","journal-title":"J Med Syst"},{"issue":"7","key":"8729_CR2","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1007\/s00521-013-1438-3","volume":"24","author":"S Mohapatra","year":"2014","unstructured":"Mohapatra S, Patra D, Satpathy S (2014) An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput Appl 24(7):1887\u20131904","journal-title":"Neural Comput Appl"},{"key":"8729_CR3","doi-asserted-by":"publisher","first-page":"104115","DOI":"10.1016\/j.compbiomed.2020.104115","volume":"128","author":"MA Morid","year":"2021","unstructured":"Morid MA, Borjali A, Del Fiol G (2021) A scoping review of transfer learning research on medical image analysis using imagenet. Comput Biol Med 128:104115","journal-title":"Comput Biol Med"},{"issue":"1","key":"8729_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-89686-3","volume":"11","author":"MA Al-Masni","year":"2021","unstructured":"Al-Masni MA, Kim DH (2021) CMM-Net: contextual multi-scale multi-level network for efficient biomedical image segmentation. Sci Rep 11(1):1\u201318","journal-title":"Sci Rep"},{"issue":"12","key":"8729_CR5","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","volume":"16","author":"JC Caicedo","year":"2019","unstructured":"Caicedo JC, Goodman A, Karhohs KW, Cimini BA, Ackerman J, Haghighi M, Heng C, Becker T, Doan M, McQuin C, Rohban M (2019) Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nat Methods 16(12):1247\u20131253","journal-title":"Nat Methods"},{"issue":"1","key":"8729_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3332-1","volume":"21","author":"F Long","year":"2020","unstructured":"Long F (2020) Microscopy cell nuclei segmentation with enhanced U-Net. BMC Bioinform 21(1):1\u201312","journal-title":"BMC Bioinform"},{"key":"8729_CR7","unstructured":"Kaggle (2018) Kaggle data science bowl challenge\u2014KDSB. Retrieved Jan 10, 2022, from https:\/\/www.kaggle.com\/c\/data-science-bowl-2018"},{"issue":"4","key":"8729_CR8","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/informatics9040076","volume":"9","author":"P Krishnadas","year":"2022","unstructured":"Krishnadas P, Chadaga K, Sampathila N, Rao S, Prabhu S (2022) Classification of malaria using object detection models. Informatics 9(4):76","journal-title":"Informatics"},{"key":"8729_CR9","doi-asserted-by":"crossref","unstructured":"Punn N S, & Agarwal S (2020) Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images.\u00a0ACM Trans Multimed Comput Commun Appl (TOMM)\u00a016(1): 1\u201315","DOI":"10.1145\/3376922"},{"key":"8729_CR10","doi-asserted-by":"publisher","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","volume":"192","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Wu C, Coleman S, Kerr D (2020) DENSE-INception U-net for medical image segmentation. Comput Methods Progr Biomed 192:105395","journal-title":"Comput Methods Progr Biomed"},{"key":"8729_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, & Rabinovich A (2015) Going deeper with convolutions. In:\u00a0proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 1\u20139)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8729_CR12","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, & Weinberger K Q (2017) Densely connected convolutional networks. In: proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 4700\u20134708)","DOI":"10.1109\/CVPR.2017.243"},{"key":"8729_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, & Sun J (2016) Deep residual learning for image recognition. In:\u00a0proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 770\u2013778)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"131","key":"8729_CR14","doi-asserted-by":"publisher","first-page":"108826","DOI":"10.1016\/j.patcog.2022.108826","volume":"1","author":"A Sharma","year":"2022","unstructured":"Sharma A, Mishra PK (2022) Covid-MANet: multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. Pattern Recognit 1(131):108826","journal-title":"Pattern Recognit"},{"key":"8729_CR15","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS (2020) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387","journal-title":"Neural Netw"},{"key":"8729_CR16","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, & Darrell T (2015). Fully convolutional networks for semantic segmentation. In:\u00a0proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 3431\u20133440)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"12","key":"8729_CR17","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8729_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, & Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: international conference on medical image computing and computer-assisted intervention\u00a0(pp 234\u2013241). Springer, Cham","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8729_CR19","unstructured":"Oktay O, Schlemper J, Folgoc L L, Lee M, Heinrich M, Misawa K, & Rueckert D (2018) Attention u-net: Learning where to look for the pancreas.\u00a0arXiv preprint arXiv:1804.03999"},{"issue":"6","key":"8729_CR20","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (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"},{"issue":"1","key":"8729_CR21","doi-asserted-by":"publisher","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","volume":"6","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. J Med Imaging 6(1):014006","journal-title":"J Med Imaging"},{"issue":"5","key":"8729_CR22","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"8729_CR23","doi-asserted-by":"crossref","unstructured":"Lou A, Guan S, Loew M (2021) DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation. In:\u00a0medical imaging 2021: image processing 11596:758\u2013768 SPIE","DOI":"10.1117\/12.2582338"},{"issue":"5","key":"8729_CR24","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1109\/JBHI.2021.3138024","volume":"26","author":"A Srivastava","year":"2021","unstructured":"Srivastava A, Jha D, Chanda S, Pal U, Johansen HD, Johansen D, Riegler MA, Ali S, Halvorsen P (2021) Msrf-net: a multi-scale residual fusion network for biomedical image segmentation. IEEE J Biomed Health Inform 26(5):2252\u20132263","journal-title":"IEEE J Biomed Health Inform"},{"key":"8729_CR25","doi-asserted-by":"crossref","unstructured":"Tomar N K, Jha D, Riegler M A, Johansen H D, Johansen D, Rittscher J & Ali S (2022) Fanet: a feedback attention network for improved biomedical image segmentation.\u00a0IEEE Trans Neural Net Learn Syst","DOI":"10.1109\/TNNLS.2022.3159394"},{"key":"8729_CR26","doi-asserted-by":"publisher","first-page":"106579","DOI":"10.1016\/j.compbiomed.2023.106579","volume":"154","author":"A Lou","year":"2023","unstructured":"Lou A, Guan S, Loew M (2023) Cfpnet-m: a light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation. Comput Biol Med 154:106579","journal-title":"Comput Biol Med"},{"key":"8729_CR27","doi-asserted-by":"crossref","unstructured":"Suman S, Tiwari AK, Singh K (2023) Computer-aided diagnostic system for hypertensive retinopathy: a review. Comput Methods Programs Biomed 240:107627","DOI":"10.1016\/j.cmpb.2023.107627"},{"issue":"4","key":"8729_CR28","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"8729_CR29","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8729_CR30","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, & Jia J (2017) Pyramid scene parsing network. In:\u00a0proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 2881\u20132890)","DOI":"10.1109\/CVPR.2017.660"},{"key":"8729_CR31","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, & Ahmadi S A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV)\u00a0(pp 565\u2013571). IEEE","DOI":"10.1109\/3DV.2016.79"},{"key":"8729_CR32","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis FI, Waldner F, Caccetta P, Wu C (2020) ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens 162:94\u2013114","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"8729_CR33","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud P H, Riegler M A, Johansen D, De Lange T, Halvorsen P & Johansen H D (2019) Resunet++: an advanced architecture for medical image segmentation. In:\u00a02019 IEEE international symposium on multimedia (ISM)\u00a0(pp 225\u20132255). IEEE","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"8729_CR34","doi-asserted-by":"publisher","first-page":"21420","DOI":"10.1109\/ACCESS.2019.2896920","volume":"7","author":"Z Zeng","year":"2019","unstructured":"Zeng Z, Xie W, Zhang Y, Lu Y (2019) RIC-Unet: an improved neural network based on Unet for nuclei segmentation in histology images. Ieee Access 7:21420\u201321428","journal-title":"Ieee Access"},{"key":"8729_CR35","unstructured":"Zhang J, Jin Y, Xu J, Xu X, & Zhang Y (2018). Mdu-net: multi-scale densely connected u-net for biomedical image segmentation.\u00a0arXiv preprint arXiv:1812.00352"},{"key":"8729_CR36","doi-asserted-by":"crossref","unstructured":"Jha D, Riegler M A, Johansen D, Halvorsen P, & Johansen H D (2020) Doubleu-net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS)\u00a0(pp 558\u2013564). IEEE","DOI":"10.1109\/CBMS49503.2020.00111"},{"issue":"10","key":"8729_CR37","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"8729_CR38","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"J Dolz","year":"2018","unstructured":"Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB (2018) HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans Med Imaging 38(5):1116\u20131126","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"8729_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-022-01280-3","volume":"33","author":"NS Punn","year":"2022","unstructured":"Punn NS, Agarwal S (2022) RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging. Mach Vis Appl 33(2):1\u201310","journal-title":"Mach Vis Appl"},{"key":"8729_CR40","doi-asserted-by":"crossref","unstructured":"Yu F, Wang D, Shelhamer E, & Darrell T (2018) Deep layer aggregation. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition\u00a0(pp 2403\u20132412)","DOI":"10.1109\/CVPR.2018.00255"},{"key":"8729_CR41","doi-asserted-by":"crossref","unstructured":"Yadavendra, Chand S (2022) Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.\u00a0Net Comput Neural Syst 1\u201320","DOI":"10.1080\/0954898X.2022.2096938"},{"key":"8729_CR42","doi-asserted-by":"crossref","unstructured":"Azad R, Asadi-Aghbolaghi M, Fathy M, & Escalera S (2019) Bi-directional ConvLSTM U-Net with densley connected convolutions. In:\u00a0proceedings of the IEEE\/CVF international conference on computer vision workshops\u00a0(pp 0-0)","DOI":"10.1109\/ICCVW.2019.00052"},{"issue":"1","key":"8729_CR43","doi-asserted-by":"publisher","first-page":"14105","DOI":"10.1038\/s41598-021-93169-w","volume":"11","author":"NR Gudhe","year":"2021","unstructured":"Gudhe NR, Behravan H, Sudah M, Okuma H, Vanninen R, Kosma VM, Mannermaa A (2021) Multi-level dilated residual network for biomedical image segmentation. Sci Rep 11(1):14105","journal-title":"Sci Rep"},{"issue":"1","key":"8729_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07885-y","volume":"12","author":"H Ashraf","year":"2022","unstructured":"Ashraf H, Waris A, Ghafoor MF, Gilani SO, Niazi IK (2022) Melanoma segmentation using deep learning with test-time augmentations and conditional random fields. Sci Rep 12(1):1\u201316","journal-title":"Sci Rep"},{"issue":"1","key":"8729_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-61808-3","volume":"10","author":"N Moshkov","year":"2020","unstructured":"Moshkov N, Mathe B, Kertesz-Farkas A, Hollandi R, Horvath P (2020) Test-time augmentation for deep learning-based cell segmentation on microscopy images. Sci Rep 10(1):1\u20137","journal-title":"Sci Rep"},{"issue":"3","key":"8729_CR46","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s00521-012-1286-6","volume":"24","author":"MR Senapati","year":"2014","unstructured":"Senapati MR, Panda G, Dash PK (2014) Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput Appl 24(3):745\u2013753","journal-title":"Neural Comput Appl"},{"issue":"1","key":"8729_CR47","doi-asserted-by":"publisher","first-page":"012017","DOI":"10.1088\/1742-6596\/2161\/1\/012017","volume":"2161","author":"K Chadaga","year":"2022","unstructured":"Chadaga K, Prabhu S, Bhat KV, Umakanth S, Sampathila N (2022) Medical diagnosis of COVID-19 using blood tests and machine learning. J Phys Conf Ser 2161(1):012017","journal-title":"J Phys Conf Ser"},{"issue":"29","key":"8729_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-022-13486-8","volume":"81","author":"A Sharma","year":"2022","unstructured":"Sharma A, Mishra PK (2022) Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images. Multimed Tools Appl 81(29):1\u201342","journal-title":"Multimed Tools Appl"},{"issue":"7","key":"8729_CR49","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1109\/TMI.2007.896925","volume":"26","author":"A Lehmussola","year":"2007","unstructured":"Lehmussola A, Ruusuvuori P, Selinummi J, Huttunen H, Yli-Harja O (2007) Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans Med Imaging 26(7):1010\u20131016","journal-title":"IEEE Trans Med Imaging"},{"issue":"21","key":"8729_CR50","doi-asserted-by":"publisher","first-page":"14991","DOI":"10.1007\/s00521-021-06134-z","volume":"33","author":"A Kaur","year":"2021","unstructured":"Kaur A, Kaur L, Singh A (2021) GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets. Neural Comput Appl 33(21):14991\u201315025","journal-title":"Neural Comput Appl"},{"issue":"1","key":"8729_CR51","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex. J Physiol 160(1):106","journal-title":"J Physiol"},{"key":"8729_CR52","unstructured":"Luo P, Ren J, Peng Z, Zhang R, & Li J (2018) Differentiable learning-to-normalize via switchable normalization.\u00a0arXiv preprint arXiv:1806.10779"},{"issue":"3","key":"8729_CR53","doi-asserted-by":"publisher","first-page":"404","DOI":"10.3390\/app9030404","volume":"9","author":"M Kola\u0159\u00edk","year":"2019","unstructured":"Kola\u0159\u00edk M, Burget R, Uher V, \u0158\u00edha K, Dutta MK (2019) Optimized high resolution 3d dense-u-net network for brain and spine segmentation. Appl Sci 9(3):404","journal-title":"Appl Sci"},{"issue":"19","key":"8729_CR54","doi-asserted-by":"publisher","first-page":"15281","DOI":"10.1007\/s00521-019-04616-9","volume":"32","author":"M Karim","year":"2020","unstructured":"Karim M, Rahman A, Jares JB, Decker S, Beyan O (2020) A snapshot neural ensemble method for cancer-type prediction based on copy number variations. Neural Comput Appl 32(19):15281\u201315299","journal-title":"Neural Comput Appl"},{"issue":"2","key":"8729_CR55","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2018","unstructured":"Naylor P, La\u00e9 M, Reyal F, Walter T (2018) Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging 38(2):448\u2013459","journal-title":"IEEE Trans Med Imaging"},{"key":"8729_CR56","doi-asserted-by":"publisher","first-page":"104349","DOI":"10.1016\/j.compbiomed.2021.104349","volume":"132","author":"A Mahbod","year":"2021","unstructured":"Mahbod A, Schaefer G, Bancher B, L\u00f6w C, Dorffner G, Ecker R, Ellinger I (2021) CryoNuSeg: a dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images. Comput Biol Med 132:104349","journal-title":"Comput Biol Med"},{"issue":"5","key":"8729_CR57","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1109\/TMI.2019.2947628","volume":"39","author":"N Kumar","year":"2019","unstructured":"Kumar N, Verma R, Anand D, Zhou Y, Onder OF, Tsougenis E, Chen H, Heng PA, Li J, Hu Z, Wang Y (2019) A multi-organ nucleus segmentation challenge. IEEE Trans Med Imaging 39(5):1380\u20131391","journal-title":"IEEE Trans Med Imaging"},{"key":"8729_CR58","unstructured":"Sharma A, Mishra PK (2021) Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. Int J Inf Tech 1\u201312"},{"key":"8729_CR59","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Gr 43:99\u2013111","journal-title":"Comput Med Imaging Gr"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08729-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08729-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08729-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T15:27:21Z","timestamp":1692026841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08729-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"references-count":59,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["8729"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08729-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,22]]},"assertion":[{"value":"14 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}