Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

• URL: http://arxiv.org/abs/2303.14845v3
• Date: Tue, 27 Jun 2023 09:22:05 GMT
• Title: Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma
• Authors: Xiaofei Wang and Stephen Price and Chao Li
• Abstract summary: We propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. We also propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma.
• Score: 9.082753496844731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma,as well as related histology and molecular markers on a multi-institutional dataset.

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