用集成学习与可解释AI提升皮肤癌分类准确率与可信度
Melanoma Classification Through Deep Ensemble Learning and Explainable AI
- 集成三种顶尖深度迁移学习模型，提升分类性能
- 结合可解释AI技术，明确模型判断依据，增强医疗可信度
- 适合医学AI研究者与临床医生参考，解决黑箱难题
黑色素瘤是最具侵袭性和致命性的皮肤癌之一，若未能在早期发现和治疗，将导致死亡。近年来，人工智能技术被用于帮助皮肤科医生实现黑色素瘤的早期检测，基于深度学习（DL）的系统已能以高准确率识别病变。然而，整个领域仍需克服可解释性瓶颈，才能充分发挥深度学习在医疗诊断中的潜力。由于深度学习模型的黑箱特性，其决策过程缺乏透明度，导致结果不可靠、难以获得信任。可解释人工智能（XAI）可通过解析AI系统的预测依据来解决这一问题。本文提出一种机器学习模型，通过集成三种前沿的深度迁移学习网络，并结合XAI技术，确保预测结果的可靠性，解释其决策基础。
原文摘要 · Abstract (English)
Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. However, the entire community must overcome the explainability limit to get the maximum benefit from DL for diagnostics in the healthcare domain. Because of the black box operation's shortcomings in DL models' decisions, there is a lack of reliability and trust in the outcomes. However, Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks, along with an approach to ensure the reliability of the predictions by utilizing XAI techniques to explain the basis of the predictions.
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