{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T18:22:10Z","timestamp":1770488530617,"version":"3.49.0"},"reference-count":51,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,12,8]]},"abstract":"<jats:p>The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1155\/2020\/8826568","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T03:29:28Z","timestamp":1607484568000},"page":"1-16","source":"Crossref","is-referenced-by-count":11,"title":["A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-4512","authenticated-orcid":true,"given":"Sarpong Kwadwo","family":"Asare","sequence":"first","affiliation":[{"name":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"You","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-6833","authenticated-orcid":true,"given":"Obed Tettey","family":"Nartey","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"2","volume-title":"Breast Cancer Facts and Figures","author":"American Cancer Society","year":"2019"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/9162464"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2008.2009441"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.12.007"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-015-3669-4"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.1405"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0177544"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"13","first-page":"770","article-title":"Very deep convolutional networks for large-scale image recognition","author":"K. 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