Transformer based multiple instance learning for weakly supervised histopathology image segmentation. (arXiv:2205.08878v1 [cs.CV])

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 …

arxiv cv image learning segmentation transformer