Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark
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TL;DR - A large-scale benchmark (2,958 adaptation runs) measuring how much test-time adaptation shifts model explanations in computational pathology, arguing explanation stability is a reliability axis distinct from accuracy. It matters because clinically deployed models adapting to staining/scanner/cohort shifts can silently lose attribution fidelity while accuracy metrics look fine.
- Scope: two histopathology benchmarks (Camelyon17, NCT CRC-HE), five architectures (CNNs through vision transformers and a pathology foundation model), seventeen TTA methods, and four attribution families.
- Frozen-backbone TTA methods leave attributions nearly unchanged; continual methods like CoTTA and RoTTA cause the largest explanation drift.
- Sensitivity is architecture-dependent — CNNs drift substantially more than transformer and foundation-model backbones — and drift grows with adaptation strength but is largely insensitive to batch size.
- Explanation stability correlates only weakly with adaptation quality: some methods preserve attributions while degrading calibration or accuracy, yielding "silent failures" missed by accuracy-only or explanation-only evaluation. Metric, protocol, and full benchmark released as code.
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Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark
TL;DR - A large-scale benchmark (2,958 adaptation runs) measuring how much test-time adaptation shifts model explanations in computational pathology, arguing explanation stability is a reliability axis distinct from accuracy. It matters because clinically deployed models adapting to staining/scanner/cohort shifts can silently lose attribution fidelity while accuracy metrics look fine.
- Scope: two histopathology benchmarks (Camelyon17, NCT CRC-HE), five architectures (CNNs through vision transformers and a pathology foundation model), seventeen TTA methods, and four attribution families.
- Frozen-backbone TTA methods leave attributions nearly unchanged; continual methods like CoTTA and RoTTA cause the largest explanation drift.
- Sensitivity is architecture-dependent — CNNs drift substantially more than transformer and foundation-model backbones — and drift grows with adaptation strength but is largely insensitive to batch size.
- Explanation stability correlates only weakly with adaptation quality: some methods preserve attributions while degrading calibration or accuracy, yielding "silent failures" missed by accuracy-only or explanation-only evaluation. Metric, protocol, and full benchmark released as code.