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arXiv · 2307.06344

The Whole Pathological Slide Classification via Weakly Supervised Learning

Qiehe Sun·Jiawen Li·Jin Xu·Junru Cheng·Tian Guan·Yonghong He
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Abstract

Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis. However, existing methods tend to focus on advanced aggregators with different structures, often overlooking the intrinsic features of H\&E pathological slides. To address this limitation, we introduced two pathological priors: nuclear heterogeneity of diseased cells and spatial correlation of pathological tiles. Leveraging the former, we proposed a data augmentation method that utilizes stain separation during extractor training via a contrastive learning strategy to obtain instance-level representations. We then described the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H\&E-stained tissue images based on pathological inductive bias, encompassing feature extraction, filtering, and aggregation. Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung dataset demonstrate that our proposed framework can effectively handle tasks related to cancer detection and differentiation of subtypes, outperforming state-of-the-art medical image classification methods based on MIL. The code will be released later.

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Qiehe Sun, Jiawen Li, Jin Xu, Junru Cheng, Tian Guan, Yonghong He. 2023-07-12. The Whole Pathological Slide Classification via Weakly Supervised Learning. https://arxiv.org/abs/2307.06344

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