June 16, 2024

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

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Self-interpretable multiple instance learning achieves competitive diagnostic accuracy while generating feature-level explanations rooted in handcrafted pathological features.
Across 3 cancer types, the linear prediction framework achieves performance matching state-of-the-art models on whole slide image classification tasks.
Benchmark evaluation combines a deep multiple instance learning framework with linear constraints, supporting faithful, pathologist-friendly whole slide image analysis.

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Benchmark study demonstrates competitive whole slide image classification across three cancer types, indicating interpretability without sacrificing performance.

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Kapse et al. (2024) studied this question.

synapsesocial.com/papers/68e64877b6db6435875d96c5 https://doi.org/10.1109/cvpr52733.2024.01067

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Implication

Benchmark study demonstrates competitive whole slide image classification across three cancer types, indicating interpretability without sacrificing performance.

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

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Implication

Cite This Study

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

Key Points

Discussion

Implication

Cite This Study

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