Contexts-Constrained Multiple Instance Learning for Histopathology Image Analysis (Oral)

Histopathology image analysis plays a very important role

in cancer diagnosis and therapeutic treatment. Existing supervised ap-

proaches for image segmentation require a large amount of high quality

manual delineations (on pixels), which is often hard to obtain. In this

paper, we propose a new algorithm along the line of weakly supervised

learning; we introduce context constraints as a prior for multiple instance

learning (ccMIL), which significantly reduces the ambiguity in weak su-

pervision (a 20% gain); our method utilizes image-level labels to learn an

integrated model to perform histopathology cancer image segmentation,

clustering, and classification. Experimental results on colon histopathol-

ogy images demonstrate the great advantages of ccMIL.

paper334.pdf
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In  International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Details

TypeInproceedings
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