Weakly supervised multiple instance learning histopathological tumor segmentation
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Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods relying on hand-crafted annotations that reduce the scope of the solutions since digital histology suffers from standardization and samples differ significantly between cancer phenotypes. To this end, in this paper, we propose a weakly supervised framework relying on weak standard clinical practice annotations, available in most medical centers. In particular, we exploit a multiple instance learning scheme providing a label for each instance, establishing a detailed segmentation of whole slide images. The potential of the framework is assessed with multi-centric data experiments using The Cancer Genome Atlas repository and the publicly available PatchCamelyon dataset. Promising results when compared with experts' annotations demonstrate the potentials of our approach.
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