  Hispathological image segmentation algorithms play a critical role in
computer aided diagnosis technology. The development of weakly supervised
segmentation algorithm alleviates the problem of medical image annotation that
it is time-consuming and labor-intensive. As a subset of weakly supervised
learning, Multiple Instance Learning (MIL) has been proven to be effective in
segmentation. However, there is a lack of related information between instances
in MIL, which limits the further improvement of segmentation performance. In
this paper, we propose a novel weakly supervised method for pixel-level
segmentation in histopathology images, which introduces Transformer into the
MIL framework to capture global or long-range dependencies. The multi-head
self-attention in the Transformer establishes the relationship between
instances, which solves the shortcoming that instances are independent of each
other in MIL. In addition, deep supervision is introduced to overcome the
limitation of annotations in weakly supervised methods and make the better
utilization of hierarchical information. The state-of-the-art results on the
colon cancer dataset demonstrate the superiority of the proposed method
compared with other weakly supervised methods. It is worth believing that there
is a potential of our approach for various applications in medical images.
