  Histopathology image analysis plays a critical role in cancer diagnosis and
treatment. To automatically segment the cancerous regions, fully supervised
segmentation algorithms require labor-intensive and time-consuming labeling at
the pixel level. In this research, we propose CAMEL, a weakly supervised
learning framework for histopathology image segmentation using only image-level
labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL
splits the image into latticed instances and automatically generates
instance-level labels. After label enrichment, the instance-level labels are
further assigned to the corresponding pixels, producing the approximate
pixel-level labels and making fully supervised training of segmentation models
possible. CAMEL achieves comparable performance with the fully supervised
approaches in both instance-level classification and pixel-level segmentation
on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the
automatic labeling methodology may benefit future weakly supervised learning
studies for histopathology image analysis.
