用视觉技术提升皮肤癌诊断可解释性，助力孟加拉基层医疗
Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
- 结合显著图与注意力图，可视化模型判断依据
- 提升深度学习在皮肤癌分类中的透明度与可信度
- 适合医疗AI落地、可解释性研究者参考
全球每年新增超过200万例皮肤癌病例，是发病率最高的癌症类型，也是孟加拉国第二常见的癌症（仅次于乳腺癌）。早期发现和治疗对改善患者预后至关重要，但孟加拉国缺乏足够数量的皮肤科医生及专业医疗人员，导致多数病例仅在晚期才被确诊。研究表明，深度学习算法能有效分类皮肤癌图像，但现有模型普遍缺乏可解释性，难以理解其决策过程，阻碍了其在临床实践中的应用。本文提出一种增强深度学习模型可解释性的方法，通过融合显著图与注意力图，可视化影响皮肤癌分类决策的关键特征，提升模型透明度，为基层医疗中的AI辅助诊断提供支持。
原文摘要 · Abstract (English)
With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial for enhancing patient outcomes; however, Bangladesh faces a shortage of dermatologists and qualified medical professionals capable of diagnosing and treating skin cancer. As a result, many cases are diagnosed only at advanced stages. Research indicates that deep learning algorithms can effectively classify skin cancer images. However, these models typically lack interpretability, making it challenging to understand their decision-making processes. This lack of clarity poses barriers to utilizing deep learning in improving skin cancer detection and treatment. In this article, we present a method aimed at enhancing the interpretability of deep learning models for skin cancer classification in Bangladesh. Our technique employs a combination of saliency maps and attention maps to visualize critical features influencing the model's diagnoses.
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