Establishing Truly Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning

• URL: http://arxiv.org/abs/2407.17157v1
• Date: Wed, 24 Jul 2024 11:00:08 GMT
• Title: Establishing Truly Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning
• Authors: Tianhang Nan, Yong Ding, Hao Quan, Deliang Li, Mingchen Zou, Xiaoyu Cui,
• Abstract summary: Multiple Instance Learning (MIL) has gained significant attention due to its ability to be trained using only slide-level diagnostic labels. Previous MIL researches have primarily focused on enhancing feature aggregators for globally analyzing WSIs, but overlook a causal relationship in diagnosis. We propose Causal Inference Multiple Instance Learning (CI-MIL) to establish the truly causal relationship between model predictions and diagnostic evidence regions.
• Score: 3.783430751544095
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the field of deep learning-driven Whole Slide Image (WSI) classification, Multiple Instance Learning (MIL) has gained significant attention due to its ability to be trained using only slide-level diagnostic labels. Previous MIL researches have primarily focused on enhancing feature aggregators for globally analyzing WSIs, but overlook a causal relationship in diagnosis: model's prediction should ideally stem solely from regions of the image that contain diagnostic evidence (such as tumor cells), which usually occupy relatively small areas. To address this limitation and establish the truly causal relationship between model predictions and diagnostic evidence regions, we propose Causal Inference Multiple Instance Learning (CI-MIL). CI-MIL integrates feature distillation with a novel patch decorrelation mechanism, employing a two-stage causal inference approach to distill and process patches with high diagnostic value. Initially, CI-MIL leverages feature distillation to identify patches likely containing tumor cells and extracts their corresponding feature representations. These features are then mapped to random Fourier feature space, where a learnable weighting scheme is employed to minimize inter-feature correlations, effectively reducing redundancy from homogenous patches and mitigating data bias. These processes strengthen the causal relationship between model predictions and diagnostically relevant regions, making the prediction more direct and reliable. Experimental results demonstrate that CI-MIL outperforms state-of-the-art methods. Additionally, CI-MIL exhibits superior interpretability, as its selected regions demonstrate high consistency with ground truth annotations, promising more reliable diagnostic assistance for pathologists.

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