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

arXiv 2401.03664 ↗First posted 8 Jan 2024eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 8 Jan 2024.

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References · 18

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6 more
  1. 2019Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inferencecited by 36
  2. 2020SCOUTER: Slot Attention-based Classifier for Explainable Image Recognitioncited by 29
  3. 2021Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behaviorcited by 29
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  5. 2022Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learningcited by 16
  6. 2021Keep CALM and Improve Visual Feature Attributioncited by 15

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