Dual-Channel Reliable Breast Ultrasound Image Classification Based on
  Explainable Attribution and Uncertainty Quantification
Shuge Lei Haonan Hu Dasheng Sun Huabin Zhang Kehong Yuan Jian Dai Jijun Tang Yan Tong
This paper focuses on the classification task of breast ultrasound images and
researches on the reliability measurement of classification results. We
proposed a dual-channel evaluation framework based on the proposed inference
reliability and predictive reliability scores. For the inference reliability
evaluation, human-aligned and doctor-agreed inference rationales based on the
improved feature attribution algorithm SP-RISA are gracefully applied.
Uncertainty quantification is used to evaluate the predictive reliability via
the Test Time Enhancement. The effectiveness of this reliability evaluation
framework has been verified on our breast ultrasound clinical dataset YBUS, and
its robustness is verified on the public dataset BUSI. The expected calibration
errors on both datasets are significantly lower than traditional evaluation
methods, which proves the effectiveness of our proposed reliability
measurement.