乳腺癌诊断：可解释人工智能（XAI）技术的全面探索
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
- Canadian Institute of Cybersecurity (CIC)（加拿大网络安全研究所）
- University of New Brunswick（新不伦瑞克大学）
- Dipartimento di Informatica, Università di Verona（威尼斯大学信息学院）
- Università di Verona（威尼斯大学）
- Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration（计算机科学系，数学与计算机科学学院，商学院）
- LIRIS, CNRS, Université Claude Bernard Lyon 1（LIRIS，国家科学研究中心，克莱尔伯恩大学 Lyon 1 分校）
- LHC, UMR5516, Université de Saint-Etienne（LHC，UMR5516，圣艾蒂安大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文探讨了XAI技术在乳腺癌诊断中的应用，分析了多种XAI方法与机器学习模型的整合，旨在提高诊断准确性与患者治疗效果。
AI中文摘要：
乳腺癌（BC）是影响全球女性最常见的恶性肿瘤之一，需要在诊断方法上取得进步以获得更好的临床结果。本文全面探讨了可解释人工智能（XAI）技术在乳腺癌检测和诊断中的应用。随着人工智能（AI）技术持续渗透到医疗领域，尤其是在肿瘤学领域，透明和可解释的模型成为增强临床决策和患者护理的必要条件。本文讨论了各种XAI方法，如SHAP、LIME、Grad-CAM等，与用于乳腺癌检测和分类的机器学习和深度学习模型的整合。通过研究乳腺癌数据集的模式，包括乳腺X线摄影、超声波及其与AI的处理，本文强调XAI如何导致更准确的诊断和个性化治疗计划。它还考察了实施这些技术的挑战以及开发标准化指标以评估XAI在临床环境中的有效性的重要性。通过详细的分析和讨论，本文旨在突出XAI在弥合复杂AI模型与实际医疗应用之间差距的潜力，从而促进医疗专业人员之间的信任和理解，并改善患者结果。
英文摘要：
Breast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. This article provides a comprehensive exploration of the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. As Artificial Intelligence (AI) technologies continue to permeate the healthcare sector, particularly in oncology, the need for transparent and interpretable models becomes imperative to enhance clinical decision-making and patient care. This review discusses the integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and others, with machine learning and deep learning models utilized in breast cancer detection and classification. By investigating the modalities of breast cancer datasets, including mammograms, ultrasounds and their processing with AI, the paper highlights how XAI can lead to more accurate diagnoses and personalized treatment plans. It also examines the challenges in implementing these techniques and the importance of developing standardized metrics for evaluating XAI's effectiveness in clinical settings. Through detailed analysis and discussion, this article aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications, thereby fostering trust and understanding among medical professionals and improving patient outcomes.
