Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma
This addresses the need for more accurate digital pathology diagnosis in cancer by integrating molecular and histology data, though it appears incremental as a first attempt in this specific context.
The paper tackled the problem of classifying diffuse glioma by jointly predicting histology features and molecular markers from whole slide images, resulting in a method that outperformed state-of-the-art approaches on a multi-institutional dataset.
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.