{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:12:10Z","timestamp":1780402330817,"version":"3.54.1"},"reference-count":76,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Continual learning (CL) is increasingly important for developing adaptive clinical AI models; however, its application to histopathology remains challenging due to privacy constraints, expanding diagnostic categories, and staining variability. We investigate CL for histopathology image classification under a class-incremental learning (CIL) scenario, where new diagnostic categories are introduced sequentially. Methods: We benchmark representative regularization-, replay-, architecture-, and prompt-based CL methods on the NCT-CRC-HE-100K dataset, with additional validation on CRC-HE-7K. We compare four normalization strategies and analyze the effects of replay buffer size and training epochs. In addition to average accuracy and forgetting, we conduct clinical relevance and error analysis using confusion matrices, ROC curves, and misclassification cases, then assess training dynamics and computational efficiency. Results: Dataset-level normalization consistently achieves the best performance among the evaluated normalization strategies. Among replay-based methods, DER++ achieves strong performance when previous-task images can be stored and replayed, reaching an average accuracy of 94.77\u00b11.82 and forgetting of 3.66\u00b11.73 with a buffer size of 500 and 50 training epochs. However, it requires higher memory usage, longer training time, and storage of previous samples. Among prompt-based methods, DualPrompt performs best with 5 epochs, reaching an average accuracy of 88.97\u00b10.60 and forgetting of 7.70\u00b11.21 while showing smoother training behavior and lower computational cost. Conclusions: Replay-based methods achieve higher accuracy and lower forgetting when exemplar storage and sufficient computational resources are available, but introduce higher computational and privacy costs. Prompt-based methods provide a competitive exemplar-free alternative under privacy- and resource-constrained settings. Dataset-level normalization is also important for stable CL performance in histopathology CIL.<\/jats:p>","DOI":"10.3390\/diagnostics16111711","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:25:31Z","timestamp":1780399531000},"page":"1711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Continual Learning for Histopathology Image Classification in Class-Incremental Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1880-5977","authenticated-orcid":false,"given":"Yuanyuan","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anca","family":"Ralescu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref_1","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. 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