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Histopathology image analysis plays a crucial role in cancer diagnosis.However, training a clinically applicable segmentation algorithm requirespathologists to engage in labour-intensive labelling. In contrast, weaklysupervised learning methods, which only require coarse-grained labels at theimage level, can significantly reduce the labeling efforts. Unfortunately,while these methods perform reasonably well in slide-level prediction, theirability to locate cancerous regions, which is essential for many clinicalapplications, remains unsatisfactory. Previously, we proposed CAMEL, whichachieves comparable results to those of fully supervised baselines inpixel-level segmentation. However, CAMEL requires 1,280x1,280 image-levelbinary annotations for positive WSIs. Here, we present CAMEL2, by introducing athreshold of the cancerous ratio for positive bags, it allows us to betterutilize the information, consequently enabling us to scale up the image-levelsetting from 1,280x1,280 to 5,120x5,120 while maintaining the accuracy. Ourresults with various datasets, demonstrate that CAMEL2, with the help of5,120x5,120 image-level binary annotations, which are easy to annotate,achieves comparable performance to that of a fully supervised baseline in bothinstance- and slide-level classifications.

CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio