Weakly supervised multiple instance learning histopathological tumor segmentation
Published in
International Conference on Medical Image Compu...(2020)External Links:
TL
TL;DR
A weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems, is proposed that exploits a multiple instance learning scheme for training models.
Abstract
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset. Promising results when compared with experts' annotations demonstrate the potentials of the presented approach. The complete framework, including $6481$ generated tumor maps and data processing, is available at \url{this https URL\_segmentation}.
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