A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM. (June 2023)

• Record Type: Journal Article
• Authors: Song, Di; Yao, Jincao; Jiang, Yitao; Shi, Siyuan; Cui, Chen; Wang, Liping; Wang, Lijing; Wu, Huaiyu; Tian, Hongtian; Ye, Xiuqin; Ou, Di; Li, Wei; Feng, Na; Pan, Weiyun; Song, Mei; Xu, Jinfeng; Xu, Dong; Wu, Linghu; Dong, Fajin
• Is Part Of: Computer methods and programs in biomedicine. Volume 235(2023)
• Publication Date: 2023-06
• Subjects: Explainability -- Artificial intelligence -- Medical decision support -- Ultrasound -- Thyroid cancer; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107527
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose an explainable framework called Explainer for thyroid nodule classification. Explainer provides physicians a tool to eval the reliability of AI. Explainer uses an intrinsic method to explain decisions. Reader studies prove that physicians achieve better performance when assisted by Explainer than when diagnosing alone. Explainer is capable of locating more reasonable and feature-related regions than the classic post-hoc technique. Abstract: Background and objective: The value of implementing artificial intelligence (AI) on ultrasound screening for thyroid cancer has been acknowledged, with numerous early studies confirming AI might help physicians acquire more accurate diagnoses. However, the black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Furthermore, explainability is not only related to AI performance, but also responsibility and risk in medical diagnosis. In this paper, we offer Explainer, an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions on which its prediction is based. Methods: A dataset of 19341 thyroid ultrasound images with pathological results and physician-annotated TI-RADS features is used to train and test the robustness of the proposed framework. Then we conducted a benign-malignant classification study to determine whether physicians perform better with the assistance of an explainer than they do alone or with Gradient-weighted Class Activation Mapping (Grad-CAM). Results: Reader studies show that the Explainer can achieve a more accurate diagnosis while explaining heatmaps, and that physicians' performances are improved when assisted by the Explainer. Case study results confirm that the Explainer is capable of locating more reasonable and feature-related regions than the Grad-CAM. Conclusions: The Explainer offers physicians a tool to understand the basis of AI predictions and evaluate their reliability, which has the potential to unbox the "black box" of medical imaging AI.