黑色瘤是迄今为止最致命的皮肤癌,仅在2019年在美国杀死了超过7,000人。早期发现这种疾病显着降低了死亡的风险和治疗成本,但普遍存在的黑色素瘤筛查目前无法是可行的。美国有大约12,000名实践皮肤病学家,他们每年都需要每年见到27,416名患者,以筛选可疑着色病变(SPL)的全部人口,可以表明癌症。
现在,由于基于卷积深度的皮肤病变的新CAD系统,已经纠正了监督。神经网络(CDNNS)由哈佛大学和马萨诸塞州的MITSACHUSTES工程学院的研究人员开发的研究人员和Massachusetts技术研究所(MIT)。新系统成功地区分了患者皮肤照片中的非可疑病变,精度〜90%,并首次建立了一个能够匹配三位皮肤科医生的共识的“丑小鸭”度量。
“We essentially provide a well-defined mathematical proxy for the deep intuition a dermatologist relies on when determining whether a skin lesion is suspicious enough to warrant closer examination,” said the study’s first author Luis Soenksen, Ph.D., a Postdoctoral Fellow at the Wyss Institute who is also a Venture Builder at MIT. “This innovation allows photos of patients’ skin to be quickly analyzed to identify lesions that should be evaluated by a dermatologist, allowing effective screening for melanoma at the population level.”
将丑小鸭带入焦点
黑色素瘤是索努克的个人,他看过几个亲密的朋友和家人患有这种疾病。“这对我来说,人们可以从黑色素瘤死亡,因为初级保健医生和患者目前没有工具有效地找到”奇数“。我决定通过利用我从我的智力从事智力和麻省理工学院的人工智能学到的许多技术来承担这个问题,“他说。
SOENKEN和他的合作者发现,为识别SPLS创建的所有现有的CAD系统仅分析病变,完全省略了皮肤科医生使用在考试期间比较患者痣的丑陋小鸭标准。所以他们决定建立自己。
确保他们的系统可以由没有专业的人使用皮肤科培训,团队创建了一个超过33,000个“宽野”图像的数据库,包括背景和其他非皮肤对象,使CDNN能够使用从消费者级摄像机获取的照片进行诊断。该图像含有标有三个董事会认证的皮肤科医生的标记和证实的SPL和不可疑的皮肤病因子。在培训数据库和随后的细化和测试后,该系统能够区分从不可疑病变的可疑,其灵敏度为90.3%和89.9%的特异性,改善以前发表的系统。
但这种基线系统仍在分析各个病变的特征,而不是在皮肤科医生这样的多个病变中的特征。To add the ugly duckling criteria into their model, the team used the extracted features in a secondary stage to create a 3D “map” of all of the lesions in a given image, and calculated how far away from “typical” each lesion’s features were. The more “odd” a given lesion was compared to the others in an image, the further away it was from the center of the 3D space. This distance is the first quantifiable definition of the ugly duckling criteria, and serves as a gateway to leveraging深度学习克服识别和仔细审查单个患者中所有着色病变之间的差异的具有挑战性和耗时的任务。
深入学习与皮肤科医生
他们的DCNN仍然不得不通过一个最终测试:表演以及生活,呼吸皮肤科医生在识别患者皮肤图像的图像的任务。三位皮肤科医生从68名患者中检查了135个宽野照片,并分配了每个病变的“奇怪”得分,表明它看起来有多有关。通过算法分析并评分相同的图像。在比较评估时,研究人员发现,该算法同意皮肤科医生的共识88%的时间,并且具有个体皮肤科医生86%的时间。
“这种高水平的共识人工智能and human clinicians is an important advance in this field, because dermatologists’ agreement with each other is typically very high, around 90%,” said co-author Jim Collins, Ph.D., a Core Faculty member of the Wyss Institute and co-leader of its Predictive BioAnalytics Initiative who is also the Termeer Professor of Medical Engineering and Science at MIT. “Essentially, we’ve been able to achieve dermatologist-level accuracy in diagnosing potential skin cancer lesions from images that can be taken by anybody with a smartphone, which opens up huge potential for finding and treating melanoma earlier.”
认识到,应尽可能多地获得这种技术,以获得最大的好处,该团队在GitHub上进行了算法开源。他们希望与医疗中心合作,推出进一步证明其系统的疗效,以及工业将其转化为世界各地的初级保健提供者可以使用的产品。他们还认识到,为了普遍乐于助人,他们的算法需要能够在整个人类肤色的全谱上进行同样良好,他们计划包含到未来的发展中。
“Allowing our scientists to purse their passions and visions is key to the success of the Wyss Institute, and it’s wonderful to see this advance that can impact all of us in such a meaningful way emerge from a collaboration with our newly formed Predictive BioAnalytics Initiative,” said Wyss Founding Director Don Ingber, M.D., Ph.D.
该技术描述于科学翻译医学,并且CDNN的源代码公开可用GitHub.。
来源:哈佛大学的Wyss学院