Rezk Eman, Eltorki Mohamed, El-Dakhakhni Wael
School of Computational Science and Engineering, McMaster University, Hamilton, ON Canada.
Faculty of Health Sciences, McMaster University, Hamilton, ON Canada.
J Healthc Inform Res. 2023 Feb 15;7(1):59-83. doi: 10.1007/s41666-023-00127-4. eCollection 2023 Mar.
The recent advances in artificial intelligence have led to the rapid development of computer-aided skin cancer diagnosis applications that perform on par with dermatologists. However, the black-box nature of such applications makes it difficult for physicians to trust the predicted decisions, subsequently preventing the proliferation of such applications in the clinical workflow. In this work, we aim to address this challenge by developing an interpretable skin cancer diagnosis approach using clinical images. Accordingly, a skin cancer diagnosis model consolidated with two interpretability methods is developed. The first interpretability method integrates skin cancer diagnosis domain knowledge, characterized by a skin lesion taxonomy, into model development, whereas the other method focuses on visualizing the decision-making process by highlighting the dominant of interest regions of skin lesion images. The proposed model is trained and validated on clinical images since the latter are easily obtainable by non-specialist healthcare providers. The results demonstrate the effectiveness of incorporating lesion taxonomy in improving model classification accuracy, where our model can predict the skin lesion origin as melanocytic or non-melanocytic with an accuracy of 87%, predict lesion malignancy with 77% accuracy, and provide disease diagnosis with an accuracy of 71%. In addition, the implemented interpretability methods assist understand the model's decision-making process and detecting misdiagnoses. This work is a step toward achieving interpretability in skin cancer diagnosis using clinical images. The developed approach can assist general practitioners to make an early diagnosis, thus reducing the redundant referrals that expert dermatologists receive for further investigations.
人工智能的最新进展推动了计算机辅助皮肤癌诊断应用的快速发展，其性能可与皮肤科医生相媲美。然而，此类应用的黑箱性质使得医生难以信任预测结果，进而阻碍了它们在临床工作流程中的推广。在这项工作中，我们旨在通过开发一种利用临床图像的可解释性皮肤癌诊断方法来应对这一挑战。为此，我们开发了一种结合了两种可解释性方法的皮肤癌诊断模型。第一种可解释性方法将以皮肤病变分类法为特征的皮肤癌诊断领域知识整合到模型开发中，而另一种方法则专注于通过突出显示皮肤病变图像中主要的感兴趣区域来可视化决策过程。由于非专业医疗服务提供者很容易获取临床图像，因此我们在临床图像上对所提出的模型进行了训练和验证。结果表明，纳入病变分类法在提高模型分类准确性方面是有效的，我们的模型能够以87%的准确率预测皮肤病变起源是黑素细胞性还是非黑素细胞性，以77%的准确率预测病变恶性程度，并以71%的准确率提供疾病诊断。此外，所实施的可解释性方法有助于理解模型的决策过程并检测误诊情况。这项工作朝着利用临床图像实现皮肤癌诊断的可解释性迈出了一步。所开发的方法可以帮助全科医生进行早期诊断，从而减少皮肤科专家因进一步检查而收到的冗余转诊。
