Weakly supervised multiple instance learning histopathological tumor segmentation

• URL: http://arxiv.org/abs/2004.05024v4
• Date: Tue, 11 May 2021 12:26:39 GMT
• Title: Weakly supervised multiple instance learning histopathological tumor segmentation
• Authors: Marvin Lerousseau, Maria Vakalopoulou, Marion Classe, Julien Adam, Enzo Battistella, Alexandre Carr\'e, Th\'eo Estienne, Th\'eophraste Henry, Eric Deutsch, Nikos Paragios
• Abstract summary: We propose a weakly supervised framework for whole slide imaging segmentation. 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.
• Score: 51.085268272912415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/marvinler/tcga_segmentation.

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