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

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Haonan Hu , Shuge Lei , Desheng Sun , Huabin Zhang , Kehong Yuan , Dai Jian , Jijun Tang , Yan Tong , Qiongyu Ye ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1145/3645259.3645269

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.

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Robustness (evolution) Computer science Reliability (semiconductor) Inference Artificial intelligence Feature extraction Channel (broadcasting) Machine learning Breast ultrasound Pattern recognition (psychology) Data mining Mammography Breast cancer Medicine Chemistry Internal medicine Physics Quantum mechanics Cancer Biochemistry Computer network Gene Power (physics)

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Type: article
Language: en
Landing Page: https://doi.org/10.1145/3645259.3645269
PDF: https://dl.acm.org/doi/pdf/10.1145/3645259.3645269
OA Status: gold
References: 36
Related Works: 10
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DOI: https://doi.org/10.1145/3645259.3645269

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

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Language: en

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Publication year: 2024

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Publication date: 2024-01-12

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Authors: Haonan Hu, Shuge Lei, Desheng Sun, Huabin Zhang, Kehong Yuan, Dai Jian, Jijun Tang, Yan Tong, Qiongyu Ye

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Landing page: https://doi.org/10.1145/3645259.3645269

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Concepts: Robustness (evolution), Computer science, Reliability (semiconductor), Inference, Artificial intelligence, Feature extraction, Channel (broadcasting), Machine learning, Breast ultrasound, Pattern recognition (psychology), Data mining, Mammography, Breast cancer, Medicine, Chemistry, Internal medicine, Physics, Quantum mechanics, Cancer, Biochemistry, Computer network, Gene, Power (physics)

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publication_date	2024-01-12
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