Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation

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.