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This paper focuses on the classification task of breast ultrasound images andresearches on the reliability measurement of classification results. Weproposed a dual-channel evaluation framework based on the proposed inferencereliability and predictive reliability scores. For the inference reliabilityevaluation, human-aligned and doctor-agreed inference rationales based on theimproved feature attribution algorithm SP-RISA are gracefully applied.Uncertainty quantification is used to evaluate the predictive reliability viathe Test Time Enhancement. The effectiveness of this reliability evaluationframework has been verified on our breast ultrasound clinical dataset YBUS, andits robustness is verified on the public dataset BUSI. The expected calibrationerrors on both datasets are significantly lower than traditional evaluationmethods, which proves the effectiveness of our proposed reliabilitymeasurement.

Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification