{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T13:13:21Z","timestamp":1771593201677,"version":"3.50.1"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["22-2389 Pj"],"award-info":[{"award-number":["22-2389 Pj"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005024","name":"Beijing Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022zz075"],"award-info":[{"award-number":["2022zz075"]}],"id":[{"id":"10.13039\/501100005024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.bspc.2026.109509","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T04:03:00Z","timestamp":1768017780000},"page":"109509","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Morphology-enhanced CAM-guided SAM for weakly supervised breast lesion segmentation"],"prefix":"10.1016","volume":"116","author":[{"given":"Xin","family":"Yue","sequence":"first","affiliation":[]},{"given":"Qing","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiaoling","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jianqiang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7265-1715","authenticated-orcid":false,"given":"Jing","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Changwei","family":"Song","sequence":"additional","affiliation":[]},{"given":"Suqin","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5765-2964","authenticated-orcid":false,"given":"Rodrigo","family":"Moreno","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5560-1684","authenticated-orcid":false,"given":"Zhikai","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6092-1395","authenticated-orcid":false,"given":"Stefano E.","family":"Romero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6474-6272","authenticated-orcid":false,"given":"Gabriel","family":"Jimenez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6391-5983","authenticated-orcid":false,"given":"Guanghui","family":"Fu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.bspc.2026.109509_b1","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.7150\/ijbs.21635","article-title":"Risk factors and preventions of breast cancer","volume":"13","author":"Sun","year":"2017","journal-title":"Int. J. Biol. Sci."},{"key":"10.1016\/j.bspc.2026.109509_b2","series-title":"Breast cancer","author":"World health organization","year":"2023"},{"issue":"1130","key":"10.1016\/j.bspc.2026.109509_b3","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20211033","article-title":"Understanding breast cancer as a global health concern","volume":"95","author":"Wilkinson","year":"2022","journal-title":"Br. J. Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b4","first-page":"31","article-title":"Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States","volume":"68","author":"Islami","year":"2018","journal-title":"CA: Cancer J. Clin."},{"issue":"7","key":"10.1016\/j.bspc.2026.109509_b5","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1002\/jcp.26379","article-title":"Breast cancer diagnosis: Imaging techniques and biochemical markers","volume":"233","author":"Jafari","year":"2018","journal-title":"J. Cell. Physiol."},{"issue":"2","key":"10.1016\/j.bspc.2026.109509_b6","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1148\/radiol.2016161444","article-title":"Supplemental breast MR imaging screening of women with average risk of breast cancer","volume":"283","author":"Kuhl","year":"2017","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s43055-020-00175-5","article-title":"A review of various modalities in breast imaging: technical aspects and clinical outcomes","volume":"51","author":"Iranmakani","year":"2020","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ultrasmedbio.2017.09.012","article-title":"Ultrasound imaging technologies for breast cancer detection and management: a review","volume":"44","author":"Guo","year":"2018","journal-title":"Ultrasound Med. Biol."},{"issue":"3","key":"10.1016\/j.bspc.2026.109509_b9","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.ejrad.2012.03.005","article-title":"Computed-aided diagnosis (CAD) in the detection of breast cancer","volume":"82","author":"Dromain","year":"2013","journal-title":"Eur. J. Radiol."},{"issue":"10","key":"10.1016\/j.bspc.2026.109509_b10","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.2174\/1573405616666200406110547","article-title":"Breast cancer detection and classification using traditional computer vision techniques: a comprehensive review","volume":"16","author":"Zahoor","year":"2020","journal-title":"Curr. Med. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.109509_b11","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: a review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPROC.2019.2932116","article-title":"Deep learning in ultrasound imaging","volume":"108","author":"Van Sloun","year":"2019","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.bspc.2026.109509_b13","series-title":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering","first-page":"1","article-title":"Fuzzy cognitive maps and a new region growing algorithm for classification of mammography images","author":"Kolahdoozi","year":"2017"},{"issue":"2","key":"10.1016\/j.bspc.2026.109509_b14","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.fcij.2018.10.005","article-title":"Benign and malignant breast cancer segmentation using optimized region growing technique","volume":"3","author":"Punitha","year":"2018","journal-title":"Futur. Comput. Inform. J."},{"key":"10.1016\/j.bspc.2026.109509_b15","series-title":"2017 2nd International Conference on Image, Vision and Computing","first-page":"366","article-title":"Automated digital mammogram segmentation using dispersed region growing and sliding window algorithm","author":"Shrivastava","year":"2017"},{"issue":"23","key":"10.1016\/j.bspc.2026.109509_b16","doi-asserted-by":"crossref","first-page":"31347","DOI":"10.1007\/s11042-018-6089-z","article-title":"A breast tumors segmentation and elimination of pectoral muscle based on hidden Markov and region growing","volume":"77","author":"El Idrissi El Kaitouni","year":"2018","journal-title":"Multimedia Tools Appl."},{"issue":"12","key":"10.1016\/j.bspc.2026.109509_b17","first-page":"292","article-title":"Breast cancer segmentation using global thresholding and region merging","volume":"6","author":"Singh","year":"2018","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"10.1016\/j.bspc.2026.109509_b18","doi-asserted-by":"crossref","first-page":"203097","DOI":"10.1109\/ACCESS.2020.3036072","article-title":"Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images","volume":"8","author":"Zebari","year":"2020","journal-title":"Ieee Access"},{"key":"10.1016\/j.bspc.2026.109509_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104373","article-title":"Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution","volume":"80","author":"Yang","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.109509_b20","series-title":"International Conference on Medical and Biological Engineering","first-page":"751","article-title":"Breast lesions detection using FADHECAL and multilevel Otsu thresholding segmentation in digital mammograms","author":"Suradi","year":"2021"},{"issue":"3","key":"10.1016\/j.bspc.2026.109509_b21","doi-asserted-by":"crossref","first-page":"78","DOI":"10.38094\/2020jastt1328","article-title":"Region of interest segmentation based on clustering techniques for breast cancer ultrasound images: A review","volume":"1","author":"Muhammad","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"10.1016\/j.bspc.2026.109509_b22","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s11548-016-1513-1","article-title":"Breast ultrasound image segmentation: a survey","volume":"12","author":"Huang","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.bspc.2026.109509_b23","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.bspc.2026.109509_b24","article-title":"A U-Net ensemble for breast lesion segmentation in DCE MRI","volume":"140","author":"Vidal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.109509_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102027","article-title":"Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network","volume":"61","author":"Byra","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.109509_b26","article-title":"AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images","volume":"42","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.109509_b27","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","article-title":"SMU-net: Saliency-guided morphology-aware U-Net for breast lesion segmentation in ultrasound image","volume":"41","author":"Ning","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.bspc.2026.109509_b28","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab5745","article-title":"AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms","volume":"65","author":"Sun","year":"2020","journal-title":"Phys. Med. Biol."},{"issue":"7","key":"10.1016\/j.bspc.2026.109509_b29","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1109\/JBHI.2023.3266977","article-title":"Batformer: Towards boundary-aware lightweight transformer for efficient medical image segmentation","volume":"27","author":"Lin","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.109509_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106629","article-title":"Hctnet: A hybrid CNN-transformer network for breast ultrasound image segmentation","volume":"155","author":"He","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b31","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.bspc.2026.109509_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101693","article-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","volume":"63","author":"Tajbakhsh","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.109509_b33","doi-asserted-by":"crossref","unstructured":"B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921\u20132929.","DOI":"10.1109\/CVPR.2016.319"},{"key":"10.1016\/j.bspc.2026.109509_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102315","article-title":"Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints","volume":"76","author":"Li","year":"2022","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.bspc.2026.109509_b35","doi-asserted-by":"crossref","first-page":"475","DOI":"10.3390\/bioengineering9090475","article-title":"NDG-CAM: nuclei detection in histopathology images with semantic segmentation networks and grad-CAM","volume":"9","author":"Altini","year":"2022","journal-title":"Bioengineering"},{"key":"10.1016\/j.bspc.2026.109509_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110592","article-title":"A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?","author":"Groen","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.bspc.2026.109509_b37","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.neucom.2021.04.044","article-title":"Attention-based full slice brain CT image diagnosis with explanations","volume":"452","author":"Fu","year":"2021","journal-title":"Neurocomputing"},{"issue":"11","key":"10.1016\/j.bspc.2026.109509_b38","doi-asserted-by":"crossref","first-page":"7054","DOI":"10.1002\/mp.15871","article-title":"Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases","volume":"49","author":"Wang","year":"2022","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.109509_b39","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","article-title":"LayerCAM: Exploring hierarchical class activation maps for localization","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.bspc.2026.109509_b40","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16","first-page":"618","article-title":"Rethinking class activation mapping for weakly supervised object localization","author":"Bae","year":"2020"},{"key":"10.1016\/j.bspc.2026.109509_b41","doi-asserted-by":"crossref","unstructured":"K. Sun, H. Shi, Z. Zhang, Y. Huang, ECS-net: Improving weakly supervised semantic segmentation by using connections between class activation maps, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 7283\u20137292.","DOI":"10.1109\/ICCV48922.2021.00719"},{"key":"10.1016\/j.bspc.2026.109509_b42","series-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.bspc.2026.109509_b43","series-title":"Segment anything model (SAM) enhanced pseudo labels for weakly supervised semantic segmentation","author":"Chen","year":"2023"},{"key":"10.1016\/j.bspc.2026.109509_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.bspc.2026.109509_b45","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.3390\/diagnostics13111947","article-title":"Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation","volume":"13","author":"Shi","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.bspc.2026.109509_b46","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"24","article-title":"Beyond adapting SAM: Towards end-to-end ultrasound image segmentation via auto prompting","author":"Lin","year":"2024"},{"key":"10.1016\/j.bspc.2026.109509_b47","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"2","key":"10.1016\/j.bspc.2026.109509_b48","doi-asserted-by":"crossref","first-page":"507","DOI":"10.3390\/make3020026","article-title":"Going to extremes: weakly supervised medical image segmentation","volume":"3","author":"Roth","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.bspc.2026.109509_b49","doi-asserted-by":"crossref","unstructured":"P.O. Pinheiro, R. Collobert, From image-level to pixel-level labeling with convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1713\u20131721.","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"10.1016\/j.bspc.2026.109509_b50","doi-asserted-by":"crossref","unstructured":"Z. Chen, Z. Tian, J. Zhu, C. Li, S. Du, C-CAM: Causal CAM for weakly supervised semantic segmentation on medical image, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11676\u201311685.","DOI":"10.1109\/CVPR52688.2022.01138"},{"key":"10.1016\/j.bspc.2026.109509_b51","doi-asserted-by":"crossref","unstructured":"Y. Zhong, J. Wang, L. Wang, J. Peng, Y.-X. Wang, L. Zhang, DAP: Detection-aware pre-training with weak supervision, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4537\u20134546.","DOI":"10.1109\/CVPR46437.2021.00451"},{"key":"10.1016\/j.bspc.2026.109509_b52","doi-asserted-by":"crossref","unstructured":"J. Ahn, S. Kwak, Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4981\u20134990.","DOI":"10.1109\/CVPR.2018.00523"},{"issue":"1","key":"10.1016\/j.bspc.2026.109509_b53","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2026.109509_b54","series-title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","author":"Wu","year":"2023"},{"key":"10.1016\/j.bspc.2026.109509_b55","series-title":"Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model","author":"Chen","year":"2023"},{"key":"10.1016\/j.bspc.2026.109509_b56","series-title":"SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image","author":"Deng","year":"2023"},{"key":"10.1016\/j.bspc.2026.109509_b57","doi-asserted-by":"crossref","unstructured":"X. Liu, J. Li, L. Zhao, C. Zhu, T. Ma, X. Xu, Q. Zhao, Anatomy-guided Weakly Supervised Breast Lesion Segmentation Fusing Contour and Semantic Information, in: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2023, pp. 7283\u20137292.","DOI":"10.1109\/SMC53992.2023.10394472"},{"key":"10.1016\/j.bspc.2026.109509_b58","doi-asserted-by":"crossref","first-page":"266","DOI":"10.5201\/ipol.2012.g-ace","article-title":"Automatic color enhancement (ACE) and its fast implementation","volume":"2","author":"Getreuer","year":"2012","journal-title":"Image Process. Line"},{"key":"10.1016\/j.bspc.2026.109509_b59","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.bspc.2026.109509_b60","doi-asserted-by":"crossref","unstructured":"K. He, X. Chen, S. Xie, Y. Li, P. Doll\u00e1r, R. Girshick, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.bspc.2026.109509_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data Brief"},{"key":"10.1016\/j.bspc.2026.109509_b62","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101880","article-title":"Breast ultrasound region of interest detection and lesion localisation","volume":"107","author":"Yap","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.109509_b63","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.109509_b64","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.bspc.2026.109509_b65","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.bspc.2026.109509_b66","unstructured":"H.G. Ramaswamy, et al., Ablation-CAM: Visual explanations for deep convolutional network via gradient-free localization, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 983\u2013991."},{"key":"10.1016\/j.bspc.2026.109509_b67","series-title":"2020 International Joint Conference on Neural Networks","first-page":"1","article-title":"Eigen-CAM: Class activation map using principal components","author":"Muhammad","year":"2020"},{"key":"10.1016\/j.bspc.2026.109509_b68","series-title":"2018 IEEE Winter Conference on Applications of Computer Vision","first-page":"839","article-title":"Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks","author":"Chattopadhay","year":"2018"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426000637?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426000637?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T12:34:57Z","timestamp":1771590897000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426000637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":68,"alternative-id":["S1746809426000637"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109509","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Morphology-enhanced CAM-guided SAM for weakly supervised breast lesion segmentation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.109509","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109509"}}