A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification
Yu Bai, Yue Mi, Yihan Su, Bo Zhang, Zheng Zhang, Jingyun Wu, Haiwen Huang, Yongping Xiong, Xiangyang Gong, Wendong Wang
Abstract
Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step masked nuclei patches supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we propose <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></u> ier <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></u> rchical <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>T</b></u> ransformer Graph Neural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Net</b></u> work (HAT-Net+) for cell graph classi- fications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96.9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/suyouooooo/HAT-Net</uri> .