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However, training a clinically applicable segmentation algorithm requires pathologists to engage in labor\u2010intensive labeling. In contrast, weakly supervised learning methods, which only require coarse\u2010grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide\u2010level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, CAMEL is proposed, which achieves comparable results to those of fully supervised baselines in pixel\u2010level segmentation. However, CAMEL requires 1280\u2009\u00d7\u20091280 image\u2010level binary annotations for positive WSIs. Here, CAMEL2 is presented, by introducing a threshold of the cancerous ratio for positive bags, it allows one to better utilize the information, consequently enabling us to scale up the image\u2010level setting from 1280\u2009\u00d7\u20091280 to 5120\u2009\u00d7\u20095120 while maintaining accuracy. The results with various datasets demonstrate that CAMEL2, with the help of 5120\u2009\u00d7\u20095120 image\u2010level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance\u2010 and slide\u2010level classifications.<\/jats:p>","DOI":"10.1002\/aisy.202300885","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T21:04:21Z","timestamp":1716498261000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CAMEL2: Enhancing Weakly Supervised Learning for Histopathology Images by Incorporating the Significance Ratio"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0616-878X","authenticated-orcid":false,"given":"Gang","family":"Xu","sequence":"first","affiliation":[{"name":"Multiscale Research Institute of Complex Systems Fudan University  Shanghai 200433 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