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