{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T01:57:37Z","timestamp":1793239057469,"version":"4.5.0"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"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":["Medical Image Analysis"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1016\/j.media.2024.103162","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:29:19Z","timestamp":1712190559000},"page":"103162","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["Focused active learning for histopathological image classification"],"prefix":"10.1016","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5823-0050","authenticated-orcid":false,"given":"Arne","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Morales-\u00c1lvarez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee AD","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4644-8874","authenticated-orcid":false,"given":"Lee A.","family":"Newberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andinet","family":"Enquobahrie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4694-8588","authenticated-orcid":false,"given":"Rafael","family":"Molina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aggelos K.","family":"Katsaggelos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2024.103162_b1","series-title":"Conference on Computer Vision and Pattern Recognition - CVPR","first-page":"481","article-title":"Latent space autoregression for novelty detection","author":"Abati","year":"2019"},{"key":"10.1016\/j.media.2024.103162_b2","series-title":"British Machine Vision Conference - BMVC","first-page":"57.1","article-title":"Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding","author":"Alex Kendall","year":"2017"},{"key":"10.1016\/j.media.2024.103162_b3","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-022-28818-3","article-title":"Active label cleaning for improved dataset quality under resource constraints","volume":"13","author":"Bernhardt","year":"2022","journal-title":"Nature Commun."},{"key":"10.1016\/j.media.2024.103162_b4","series-title":"ACM SIGMOD International Conference on Management of Data","first-page":"93","article-title":"LOF: Identifying density-based local outliers","author":"Breunig","year":"2000"},{"issue":"1","key":"10.1016\/j.media.2024.103162_b5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","article-title":"Artificial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge","volume":"28","author":"Bulten","year":"2022","journal-title":"Nature Med."},{"issue":"8","key":"10.1016\/j.media.2024.103162_b6","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nature Med."},{"key":"10.1016\/j.media.2024.103162_b7","first-page":"20","article-title":"Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs","volume":"vol. 11435","author":"Carse","year":"2019"},{"key":"10.1016\/j.media.2024.103162_b8","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI","first-page":"519","article-title":"Multiple instance learning with center embeddings for histopathology classification","author":"Chikontwe","year":"2020"},{"issue":"6","key":"10.1016\/j.media.2024.103162_b9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","article-title":"The mnist database of handwritten digit images for machine learning research","volume":"29","author":"Deng","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"10.1016\/j.media.2024.103162_b10","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","article-title":"Aleatory or epistemic? Does it matter?","volume":"31","author":"Der Kiureghian","year":"2009","journal-title":"Struct. Saf."},{"key":"10.1016\/j.media.2024.103162_b11","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3389\/fmed.2019.00264","article-title":"Deep learning for whole slide image analysis: An overview","volume":"6","author":"Dimitriou","year":"2019","journal-title":"Front. Med."},{"issue":"22","key":"10.1016\/j.media.2024.103162_b12","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Ehteshami Bejnordi","year":"2017","journal-title":"JAMA"},{"issue":"7639","key":"10.1016\/j.media.2024.103162_b13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.media.2024.103162_b14","unstructured":"Gal, Y., Islam, R., Ghahramani, Z., 2017. Deep Bayesian Active Learning with Image Data. In: International Conference on Machine Learning - ICML. pp. 1183\u20131192."},{"issue":"6","key":"10.1016\/j.media.2024.103162_b15","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","article-title":"Deep label distribution learning with label ambiguity","volume":"26","author":"Gao","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.media.2024.103162_b16","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X 16","first-page":"510","article-title":"Consistency-based semi-supervised active learning: Towards minimizing labeling cost","author":"Gao","year":"2020"},{"issue":"22","key":"10.1016\/j.media.2024.103162_b17","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"J. Am. Med. Assoc. -AMA"},{"key":"10.1016\/j.media.2024.103162_b18","series-title":"Conference on Computer Vision and Pattern Recognition - CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.media.2024.103162_b19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ejca.2019.06.012","article-title":"Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images","volume":"118","author":"Hekler","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"10.1016\/j.media.2024.103162_b20","series-title":"Bayesian active learning for classification and preference learning, CoRR abs\/1112.5745","author":"Houlsby","year":"2011"},{"key":"10.1016\/j.media.2024.103162_b21","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, T., Xiong, H., Huan, J., Dou, D., 2021. Semi-supervised active learning with temporal output discrepancy. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3447\u20133456.","DOI":"10.1109\/ICCV48922.2021.00343"},{"key":"10.1016\/j.media.2024.103162_b22","unstructured":"Johnson, T., Kwok, I., Ng, R.T., 1998. Fast Computation of 2-Dimensional Depth Contours.. In: International Conference on Knowledge Discovery and Data Mining. pp. 224\u2013228."},{"key":"10.1016\/j.media.2024.103162_b23","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2372","article-title":"Multi-class active learning for image classification","author":"Joshi","year":"2009"},{"key":"10.1016\/j.media.2024.103162_b24","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2022.3176091","article-title":"The devil is in the details: Whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: A review","volume":"10","author":"Kanwal","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.media.2024.103162_b25","series-title":"Neural Information Processing Systems - NeurIPS, Vol. 30","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"Kendall","year":"2017"},{"key":"10.1016\/j.media.2024.103162_b26","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.media.2024.103162_b27","unstructured":"Kingma, D.P., Salimans, T., Welling, M., 2015. Variational Dropout and the Local Reparameterization Trick. In: International Conference on Neural Information Processing Systems - NIPS. pp. 2575\u20132583."},{"key":"10.1016\/j.media.2024.103162_b28","unstructured":"Knorr, E.M., Ng, R.T., 1998. Algorithms for Mining Distance-Based Outliers in Large Datasets.. In: Gupta, A., Shmueli, O., Widom, J. (Eds.), International Conference on Very Large Databases - VLDB. pp. 392\u2013403."},{"key":"10.1016\/j.media.2024.103162_b29","unstructured":"Knorr, E.M., Ng, R.T., 1999. Finding Intensional Knowledge of Distance-Based Outliers.. In: Atkinson, M.P., Orlowska, M.E., Valduriez, P., Zdonik, S.B., Brodie, M.L. (Eds.), International Conference on Very Large Databases - VLDB. pp. 211\u2013222."},{"key":"10.1016\/j.media.2024.103162_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2019.106816","article-title":"Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation","volume":"142","author":"Kwon","year":"2020","journal-title":"Comput. Statist. Data Anal."},{"issue":"4","key":"10.1016\/j.media.2024.103162_b31","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1158\/0008-5472.CAN-20-0668","article-title":"Interactive classification of whole-slide imaging data for cancer researchers","volume":"81","author":"Lee","year":"2021","journal-title":"Cancer Res."},{"key":"10.1016\/j.media.2024.103162_b32","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J., 2018. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In: International Conference on Neural Information Processing Systems - NeurIPS. pp. 7167\u20137177."},{"key":"10.1016\/j.media.2024.103162_b33","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"859","article-title":"Adaptive active learning for image classification","author":"Li","year":"2013"},{"key":"10.1016\/j.media.2024.103162_b34","series-title":"Conference on Computer Vision and Pattern Recognition - CVPR","first-page":"14313","article-title":"Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning","author":"Li","year":"2021"},{"key":"10.1016\/j.media.2024.103162_b35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.compmedimag.2018.08.003","article-title":"An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies","volume":"69","author":"Li","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.media.2024.103162_b36","series-title":"Medical Imaging 2020: Digital Pathology","first-page":"18","article-title":"Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)","author":"Lu","year":"2020"},{"issue":"9","key":"10.1016\/j.media.2024.103162_b37","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1093\/bioinformatics\/btw013","article-title":"Collaborative analysis of multi-gigapixel imaging data using cytomine","volume":"32","author":"Mar\u00e9e","year":"2016","journal-title":"Bioinformatics"},{"key":"10.1016\/j.media.2024.103162_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102165","article-title":"Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification","volume":"73","author":"Marini","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2024.103162_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106828","article-title":"Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes","volume":"220","author":"Meirelles","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2024.103162_b40","series-title":"Deep deterministic uncertainty: A simple baseline","author":"Mukhoti","year":"2021"},{"key":"10.1016\/j.media.2024.103162_b41","series-title":"International Conference on Discovery Science","first-page":"72","article-title":"Epistemic uncertainty sampling","author":"Nguyen","year":"2019"},{"key":"10.1016\/j.media.2024.103162_b42","series-title":"Out of distribution data detection using dropout Bayesian neural networks","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.media.2024.103162_b43","series-title":"International Conference on Neural Information Processing Systems - NIPS","first-page":"3239","article-title":"Realistic evaluation of deep semi-supervised learning algorithms","author":"Oliver","year":"2018"},{"key":"10.1016\/j.media.2024.103162_b44","first-page":"193","article-title":"Semi-weakly supervised learning for prostate cancer image classification with teacher-student deep convolutional networks","volume":"12446 LNCS","author":"Ot\u00e1lora","year":"2020","journal-title":"Interpret. Annot.-Effic. Learn. Med. Image Comput."},{"issue":"1","key":"10.1016\/j.media.2024.103162_b45","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1038\/s41598-019-50587-1","article-title":"ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning","volume":"9","author":"R\u0105czkowski","year":"2019","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.media.2024.103162_b46","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0167-9473(96)00027-8","article-title":"Computing depth contours of bivariate point clouds","volume":"23","author":"Ruts","year":"1996","journal-title":"Comput. Statist. Data Anal."},{"key":"10.1016\/j.media.2024.103162_b47","doi-asserted-by":"crossref","first-page":"9763","DOI":"10.1109\/ACCESS.2022.3143345","article-title":"Efficient cancer classification by coupling semi supervised and multiple instance learning","volume":"10","author":"Schmidt","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.media.2024.103162_b48","unstructured":"Sener, O., Savarese, S., 2018. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In: International Conference on Learning Representations."},{"key":"10.1016\/j.media.2024.103162_b49","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"issue":"12","key":"10.1016\/j.media.2024.103162_b50","doi-asserted-by":"crossref","first-page":"2974","DOI":"10.3390\/cancers14122974","article-title":"A pragmatic machine learning approach to quantify tumor-infiltrating lymphocytes in whole slide images","volume":"14","author":"Shvetsov","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.media.2024.103162_b51","series-title":"International Conference on Machine Learning - ICML","article-title":"Out-of-distribution detection with deep nearest neighbors","author":"Sun","year":"2022"},{"key":"10.1016\/j.media.2024.103162_b52","unstructured":"Tan, M., Le, Q., 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: International Conference on Machine Learning - ICML, Vol. 97. pp. 6105\u20136114."},{"key":"10.1016\/j.media.2024.103162_b53","doi-asserted-by":"crossref","unstructured":"Wu, Y., Schmidt, A., Hern\u00e1ndez-S\u00e1nchez, E., Molina, R., Katsaggelos, A.K., 2021. Combining Attention-Based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI, Vol. 12902. pp. 582\u2013591.","DOI":"10.1007\/978-3-030-87196-3_54"},{"key":"10.1016\/j.media.2024.103162_b54","series-title":"Wat heb je gezegd? Detecting out-of-distribution translations with variational transformers","author":"Xiao","year":"2019"},{"key":"10.1016\/j.media.2024.103162_b55","article-title":"The relevance of Bayesian layer positioning to model uncertainty in deep Bayesian active learning","author":"Zeng","year":"2018","journal-title":"Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.media.2024.103162_b56","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/s42256-019-0052-1","article-title":"Pathologist-level interpretable whole-slide cancer diagnosis with deep learning","volume":"1","author":"Zhang","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.media.2024.103162_b57","unstructured":"Zhu, X., Lafferty, J., Ghahramani, Z., 2003. Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: International Conference on Machine Learning - ICML. pp. 58\u201365."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000872?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000872?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T04:16:53Z","timestamp":1716869813000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841524000872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":57,"alternative-id":["S1361841524000872"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2024.103162","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Focused active learning for histopathological image classification","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2024.103162","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103162"}}