{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T12:21:19Z","timestamp":1762604479488,"version":"3.40.5"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Vision and Image Understanding"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.cviu.2024.104065","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T18:10:55Z","timestamp":1719943855000},"page":"104065","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Enhanced dual contrast representation learning with cell separation and merging for breast cancer diagnosis"],"prefix":"10.1016","volume":"247","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4304-4519","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yiqi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zhehao","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Jinshan","family":"Pan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7314-6754","authenticated-orcid":false,"given":"Juncheng","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5626-7076","authenticated-orcid":false,"given":"Ming","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Lihua","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tieyong","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.cviu.2024.104065_b1","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","article-title":"Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network","volume":"32","author":"Alom","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.cviu.2024.104065_b2","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.media.2019.05.010","article-title":"Bach: Grand challenge on breast cancer histology images","volume":"56","author":"Aresta","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cviu.2024.104065_b3","doi-asserted-by":"crossref","first-page":"24680","DOI":"10.1109\/ACCESS.2018.2831280","article-title":"Classification of breast cancer based on histology images using convolutional neural networks","volume":"6","author":"Bardou","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.cviu.2024.104065_b4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.044","article-title":"Breakhis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights","volume":"375","author":"Benhammou","year":"2020","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.cviu.2024.104065_b5","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1023\/B:VISI.0000045324.43199.43","article-title":"Lucas\/kanade meets horn\/schunck: Combining local and global optic flow methods","volume":"61","author":"Bruhn","year":"2005","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.cviu.2024.104065_b6","doi-asserted-by":"crossref","unstructured":"Chao,\u00a0S., Belanger,\u00a0D., 2021. Generalizing few-shot classification of whole-genome doubling across cancer types. In: ICCV. pp. 3382\u20133392.","DOI":"10.1142\/9789811250477_0014"},{"key":"10.1016\/j.cviu.2024.104065_b7","doi-asserted-by":"crossref","unstructured":"Chen,\u00a0X., He,\u00a0K., 2021. Exploring simple siamese representation learning. In: CVPR. pp. 15750\u201315758.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"10.1016\/j.cviu.2024.104065_b8","unstructured":"Chen,\u00a0T., Kornblith,\u00a0S., Norouzi,\u00a0M., Hinton,\u00a0G., 2020. A simple framework for contrastive learning of visual representations. In: ICML. pp. 1597\u20131607."},{"key":"10.1016\/j.cviu.2024.104065_b9","doi-asserted-by":"crossref","unstructured":"Cubuk,\u00a0E.D., Zoph,\u00a0B., Man\u00e9,\u00a0D., Vasudevan,\u00a0V., Le,\u00a0Q.V., 2019. Autoaugment: Learning augmentation strategies from data. In: CVPR. pp. 113\u2013123.","DOI":"10.1109\/CVPR.2019.00020"},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-018-0057-x","article-title":"Transfer learning based histopathologic image classification for breast cancer detection","volume":"6","author":"Deniz","year":"2018","journal-title":"Health Inf. Sci. Syst."},{"year":"2020","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","key":"10.1016\/j.cviu.2024.104065_b11"},{"key":"10.1016\/j.cviu.2024.104065_b12","doi-asserted-by":"crossref","unstructured":"Frid-Adar,\u00a0M., Ben-Cohen,\u00a0A., Amer,\u00a0R., Greenspan,\u00a0H., 2018. Improving the segmentation of anatomical structures in chest radiographs using u-net with an imagenet pre-trained encoder. In: Image Analysis for Moving Organ, Breast, and Thoracic Images. pp. 159\u2013168.","DOI":"10.1007\/978-3-030-00946-5_17"},{"issue":"3","key":"10.1016\/j.cviu.2024.104065_b13","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1002\/ima.22403","article-title":"Residual learning based cnn for breast cancer histopathological image classification","volume":"30","author":"Gour","year":"2020","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"4","key":"10.1016\/j.cviu.2024.104065_b14","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1109\/JBHI.2018.2882647","article-title":"Densely-connected multi-magnification hashing for histopathological image retrieval","volume":"23","author":"Gu","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cviu.2024.104065_b15","unstructured":"Gutmann,\u00a0M., Hyv\u00e4rinen,\u00a0A., 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In: AISTATS. pp. 297\u2013304."},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b16","first-page":"1","article-title":"Breast cancer multi-classification from histopathological images with structured deep learning model","volume":"7","author":"Han","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cviu.2024.104065_b17","doi-asserted-by":"crossref","unstructured":"He,\u00a0K., Fan,\u00a0H., Wu,\u00a0Y., Xie,\u00a0S., Girshick,\u00a0R., 2020. Momentum contrast for unsupervised visual representation learning. In: CVPR. pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.cviu.2024.104065_b18","doi-asserted-by":"crossref","unstructured":"He,\u00a0K., Zhang,\u00a0X., Ren,\u00a0S., Sun,\u00a0J., 2016. Deep residual learning for image recognition. In: CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"10.1016\/j.cviu.2024.104065_b19","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1002\/ima.22548","article-title":"Classification of breast cancer histopathological image with deep residual learning","volume":"31","author":"Hu","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.cviu.2024.104065_b20","doi-asserted-by":"crossref","unstructured":"Huang,\u00a0G., Liu,\u00a0Z., Van Der\u00a0Maaten,\u00a0L., Weinberger,\u00a0K.Q., 2017. Densely connected convolutional networks. In: CVPR. pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.cviu.2024.104065_b21","doi-asserted-by":"crossref","unstructured":"Kendall,\u00a0A., Gal,\u00a0Y., Cipolla,\u00a0R., 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR. pp. 7482\u20137491.","DOI":"10.1109\/CVPR.2018.00781"},{"year":"2014","series-title":"Adam: A method for stochastic optimization","author":"Kingma","key":"10.1016\/j.cviu.2024.104065_b22"},{"year":"2023","series-title":"Segment anything","author":"Kirillov","key":"10.1016\/j.cviu.2024.104065_b23"},{"key":"10.1016\/j.cviu.2024.104065_b24","first-page":"1","article-title":"Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks","author":"Kumar","year":"2021","journal-title":"Evol. Intell."},{"key":"10.1016\/j.cviu.2024.104065_b25","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ins.2019.08.072","article-title":"Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer","volume":"508","author":"Kumar","year":"2020","journal-title":"Inform. Sci."},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1365-2818.2008.02016.x","article-title":"Segmentation of touching cell nuclei using gradient flow tracking","volume":"231","author":"Li","year":"2008","journal-title":"J. Microsc."},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2121-8-40","article-title":"3D cell nuclei segmentation based on gradient flow tracking","volume":"8","author":"Li","year":"2007","journal-title":"BMC Cell Biol."},{"key":"10.1016\/j.cviu.2024.104065_b28","doi-asserted-by":"crossref","unstructured":"Lin,\u00a0T.-Y., Goyal,\u00a0P., Girshick,\u00a0R., He,\u00a0K., Doll\u00e1r,\u00a0P., 2017. Focal loss for dense object detection. In: ICCV. pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.cviu.2024.104065_b29","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2022.3187765","article-title":"A deep learning method for breast cancer classification in the pathology images","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cviu.2024.104065_b30","doi-asserted-by":"crossref","unstructured":"Liu,\u00a0Z., Lin,\u00a0Y., Cao,\u00a0Y., Hu,\u00a0H., Wei,\u00a0Y., Zhang,\u00a0Z., Lin,\u00a0S., Guo,\u00a0B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.cviu.2024.104065_b31","doi-asserted-by":"crossref","unstructured":"Liu,\u00a0Y., Pan,\u00a0J., Su,\u00a0Z., 2019. Deep blind image inpainting. In: IScIDE. pp. 128\u2013141.","DOI":"10.1007\/978-3-030-36189-1_11"},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b32","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","article-title":"Deep learning earth observation classification using imagenet pretrained networks","volume":"13","author":"Marmanis","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"10.1016\/j.cviu.2024.104065_b33","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.icte.2018.10.007","article-title":"Breast cancer histology images classification: Training from scratch or transfer learning?","volume":"4","author":"Mehra","year":"2018","journal-title":"ICT Express"},{"key":"10.1016\/j.cviu.2024.104065_b34","article-title":"Deepusps: Deep robust unsupervised saliency prediction via self-supervision","volume":"vol. 32","author":"Nguyen","year":"2019"},{"key":"10.1016\/j.cviu.2024.104065_b35","unstructured":"v.\u00a0d. Oord,\u00a0A., Li,\u00a0Y., Vinyals,\u00a0O., 2018. Representation learning with contrastive predictive coding.."},{"issue":"5","key":"10.1016\/j.cviu.2024.104065_b36","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/JBHI.2018.2885134","article-title":"Label-efficient breast cancer histopathological image classification","volume":"23","author":"Qi","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cviu.2024.104065_b37","doi-asserted-by":"crossref","unstructured":"Qin,\u00a0X., Zhang,\u00a0Z., Huang,\u00a0C., Gao,\u00a0C., Dehghan,\u00a0M., Jagersand,\u00a0M., 2019. Basnet: Boundary-aware salient object detection. In: CVPR.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"10.1016\/j.cviu.2024.104065_b38","doi-asserted-by":"crossref","first-page":"64331","DOI":"10.1109\/ACCESS.2020.2984522","article-title":"A comparative evaluation of texture features for semantic segmentation of breast histopathological images","volume":"8","author":"Rashmi","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.cviu.2024.104065_b39","doi-asserted-by":"crossref","unstructured":"Reza,\u00a0M.S., Ma,\u00a0J., 2018. Imbalanced histopathological breast cancer image classification with convolutional neural network. In: ICSP. pp. 619\u2013624.","DOI":"10.1109\/ICSP.2018.8652304"},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b40","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3390\/diagnostics13010103","article-title":"Efficient breast cancer classification network with dual squeeze and excitation in histopathological images","volume":"13","author":"Sarker","year":"2022","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b41","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/ima.22465","article-title":"Breast cancer histopathology image classification using kernelized weighted extreme learning machine","volume":"31","author":"Saxena","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.cviu.2024.104065_b42","doi-asserted-by":"crossref","unstructured":"Shen,\u00a0P., Qin,\u00a0W., Yang,\u00a0J., Hu,\u00a0W., Chen,\u00a0S., Li,\u00a0L., Wen,\u00a0T., Gu,\u00a0J., 2015. Segmenting multiple overlapping nuclei in h & e stained breast cancer histopathology images based on an improved watershed. In: ICBISP. pp. 1\u20134.","DOI":"10.1049\/cp.2015.0779"},{"key":"10.1016\/j.cviu.2024.104065_b43","doi-asserted-by":"crossref","unstructured":"Shu,\u00a0W., Wang,\u00a0S., Chen,\u00a0Q., Hu,\u00a0Y., Cai,\u00a0Z., Lin,\u00a0R., 2019. Pathological image classification of breast cancer based on residual network and focal loss. In: CSAI. pp. 211\u2013214.","DOI":"10.1145\/3374587.3374634"},{"year":"2014","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","key":"10.1016\/j.cviu.2024.104065_b44"},{"key":"10.1016\/j.cviu.2024.104065_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108111","article-title":"Metamed: Few-shot medical image classification using gradient-based meta-learning","volume":"120","author":"Singh","year":"2021","journal-title":"Pattern Recognit."},{"issue":"7","key":"10.1016\/j.cviu.2024.104065_b46","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","article-title":"A dataset for breast cancer histopathological image classification","volume":"63","author":"Spanhol","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cviu.2024.104065_b47","doi-asserted-by":"crossref","unstructured":"Szegedy,\u00a0C., Liu,\u00a0W., Jia,\u00a0Y., Sermanet,\u00a0P., Reed,\u00a0S., Anguelov,\u00a0D., Erhan,\u00a0D., Vanhoucke,\u00a0V., Rabinovich,\u00a0A., 2015. Going deeper with convolutions. In: CVPR. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.cviu.2024.104065_b48","doi-asserted-by":"crossref","unstructured":"Tang,\u00a0Y., Han,\u00a0K., Guo,\u00a0J., Xu,\u00a0C., Li,\u00a0Y., Xu,\u00a0C., Wang,\u00a0Y., 2022. An image patch is a wave: Phase-aware vision mlp. In: CVPR. pp. 10935\u201310944.","DOI":"10.1109\/CVPR52688.2022.01066"},{"key":"10.1016\/j.cviu.2024.104065_b49","first-page":"24261","article-title":"Mlp-mixer: An all-mlp architecture for vision","volume":"vol. 34","author":"Tolstikhin","year":"2021"},{"key":"10.1016\/j.cviu.2024.104065_b50","doi-asserted-by":"crossref","unstructured":"Van\u00a0Gansbeke,\u00a0W., Vandenhende,\u00a0S., Georgoulis,\u00a0S., Van\u00a0Gool,\u00a0L., 2021. Unsupervised semantic segmentation by contrasting object mask proposals. In: ICCV.","DOI":"10.1109\/ICCV48922.2021.00990"},{"key":"10.1016\/j.cviu.2024.104065_b51","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.cviu.2024.104065_b52","doi-asserted-by":"crossref","unstructured":"Wang,\u00a0X., Li,\u00a0J., Lu,\u00a0Y., 2021. Multi-classification of histopathological images based on convolutional neural networks. In: ICFEICT. pp. 1\u20135.","DOI":"10.1145\/3474198.3478177"},{"issue":"1","key":"10.1016\/j.cviu.2024.104065_b53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"10.1016\/j.cviu.2024.104065_b54","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/4605191","article-title":"Comparison of transferred deep neural networks in ultrasonic breast masses discrimination","volume":"2018","author":"Xiao","year":"2018","journal-title":"BioMed Res. Int."},{"key":"10.1016\/j.cviu.2024.104065_b55","article-title":"Image denoising and inpainting with deep neural networks","volume":"vol. 25","author":"Xie","year":"2012"},{"key":"10.1016\/j.cviu.2024.104065_b56","doi-asserted-by":"crossref","unstructured":"Yan,\u00a0R., Ren,\u00a0F., Wang,\u00a0Z., Wang,\u00a0L., Ren,\u00a0Y., Liu,\u00a0Y., Rao,\u00a0X., Zheng,\u00a0C., Zhang,\u00a0F., 2018. A hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification. In: BIBM. pp. 957\u2013962.","DOI":"10.1109\/BIBM.2018.8621429"},{"key":"10.1016\/j.cviu.2024.104065_b57","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ymeth.2019.06.014","article-title":"Breast cancer histopathological image classification using a hybrid deep neural network","volume":"173","author":"Yan","year":"2020","journal-title":"Methods"},{"year":"2019","series-title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","author":"Yun","key":"10.1016\/j.cviu.2024.104065_b58"},{"key":"10.1016\/j.cviu.2024.104065_b59","article-title":"Aggregated contextual transformations for high-resolution image inpainting","author":"Zeng","year":"2022","journal-title":"IEEE Trans. Vis. Comput. Graphics"},{"year":"2018","series-title":"mixup: Beyond empirical risk minimization","author":"Zhang","key":"10.1016\/j.cviu.2024.104065_b60"},{"key":"10.1016\/j.cviu.2024.104065_b61","doi-asserted-by":"crossref","unstructured":"Zhou,\u00a0B., Khosla,\u00a0A., Lapedriza,\u00a0A., Oliva,\u00a0A., Torralba,\u00a0A., 2016. Learning deep features for discriminative localization. In: CVPR. pp. 2921\u20132929.","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Computer Vision and Image Understanding"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1077314224001462?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1077314224001462?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T09:05:30Z","timestamp":1732352730000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1077314224001462"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":61,"alternative-id":["S1077314224001462"],"URL":"https:\/\/doi.org\/10.1016\/j.cviu.2024.104065","relation":{},"ISSN":["1077-3142"],"issn-type":[{"type":"print","value":"1077-3142"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhanced dual contrast representation learning with cell separation and merging for breast cancer diagnosis","name":"articletitle","label":"Article Title"},{"value":"Computer Vision and Image Understanding","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cviu.2024.104065","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104065"}}