Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.
Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.
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DOI：
10.1117/1.jbo.25.11.112906
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发表时间：
2020-11
影响因子：
3.5
通讯作者：
Krueger JG
中科院分区：
文献类型：
--
作者：
Gareau DS;Browning J;Correa Da Rosa J;Suarez-Farinas M;Lish S;Zong AM;Firester B;Vrattos C;Renert-Yuval Y;Gamboa M;Vallone MG;Barragán-Estudillo ZF;Tamez-Peña AL;Montoya J;Jesús-Silva MA;Carrera C;Malvehy J;Puig S;Marghoob A;Carucci JA;Krueger JG
重要性：黑色素瘤是一种致命的癌症，医生很难早期诊断，因为他们缺乏区分良性和恶性病变的知识。图像分析的深度机器学习方法提供了希望，但缺乏作为独立诊断广泛采用的透明度。目标：我们的目标是创建一种透明的机器学习技术（即，而不是深度学习）来区分皮肤镜图像中的黑色素瘤和痣，以及用于感觉提示整合的界面。方法：成像生物标志物线索（IBC）提供集成机器学习分类器（Eclass）训练，而原始图像提供深度学习分类器训练。我们比较了诊断接受者操作曲线下的面积。结果：我们的可解释机器学习算法在75%的时间内优于领先的深度学习方法。用户界面仅将诊断成像生物标志物显示为IBC。结论：从翻译的角度来看，Eclass比卷积机器学习诊断更好，因为医生可以比黑盒输出更快地接受它。成像生物标志物线索可以在临床筛选中的感觉线索整合期间使用。我们的方法可以应用于其他基于图像的诊断分析，包括病理学和放射学。
Significance: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. Aim: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. Approach: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. Results: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. Conclusions: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.