Sensory Cue Integration in Melanoma Screening

成像生物标记物是图像中具有生物学意义的特征。例如，在一张人的照片中， 红头发，红头发是一种特征，这意味着MC1R基因存在突变，提供指令 制造一种名为黑素皮质素1受体的蛋白质。这一特征是一种成像生物标记物，可以用作医学线索 以表明患黑色素瘤的风险增加。当在此上下文中使用时，该成像生物标记物成为成像 生物标记物提示(IBC)，从某种意义上说，它可以提示医学专业观察者相应地改变治疗，例如 建议使用防晒霜。IBCS并不单独承担医疗决策的全部重量，而是 集成的。IBC分析可以是感觉线索整合的过程，也可以是观察和整合的过程 通过数码相机和计算机等技术。后者的一个优点是计算可伸缩性能够 机器视觉来计算IBC的巨大排列，这将是人类观察者无法抗拒的。因此，计算机 在选择最好的一种回教给人类之前，可以快速尝试许多潜在的诊断方法。目的 该项目是开发双向教学的人机界面，以便皮肤科专家可以教授 计算机他们使用什么IBCs来实现准确的诊断，计算机可以教皮肤科医生使用的最佳方法 并建议将机器学习引导到的新的IBC进行集成。作为结果，我们将衡量 接受IBC培训的皮肤科医生在检测黑色素瘤方面的诊断能力。众所周知，很早就 检测挽救了生命，但技术的潜力改善了早期检测，这是一个巨大的需求，因为1万美国人仍然 每年死于黑色素瘤的人数不详。这个项目将帮助回答这个未知的问题，如果我们在 用通勤者的视觉和机器学习来翻译IBCs，更多的黑色素瘤将被及早发现，生活将 得救了。我们的长期目标是通过帮助临床医生增加 基于皮肤镜的黑色素瘤筛查的预测价值。我们相信皮肤镜检查的敏感性和特异性- 基于非专家筛查者的黑色素瘤筛查可以通过辅助技术来改进，这是非常可取的 考虑到假阳性的成本(患者压力和不必要的活检)和假阴性的极高成本 (延迟黑色素瘤治疗)。

Imaging biomarkers are features in images that have biological implications. For example, in a picture of a person with red hair, the red hair is a feature and the implication is that there is a mutation in the MC1R gene that provides instructions for making a protein called the melanocortin 1 receptor. This feature, an imaging biomarker, can be used as a medical cue to indicate increased risk for melanoma. When used in this context, this imaging biomarker becomes an imaging biomarker cue (IBC), in the sense that it may cue the medical professional observer to alter treatment accordingly, such as recommending sunscreen use. IBCs do not individually bear the full weight of medical decision-making and instead are integrated. IBC analysis may be a process of sensory cue integration or may be a process of observation and integration by technology such as a digital camera and computer. An advantage of the latter is that computational scalability enables machine vision to compute vast permutations of IBCs that would be overwhelming to a human observer. Thus computers can try many potential diagnostic methods rapidly before picking the best one to teach back to humans. The purpose of this project is to develop a human/machine interface for bi-directional teaching so expert dermatologists can teach computers what IBCs they use to achieve accurate diagnosis and computers can teach dermatologists the best way to use current IBCs and suggest integration of new IBCs that machine learning guides them to. As an outcome, we will measure the diagnostic performance of dermatologists who undergo IBC training in detecting melanoma. It is known that early detection saves lives, but the potential of technology to improve early detection, a great need since 10,000 Americans still die each year from melanoma, is unknown. This project will help answer that unknown and if we are successful in translating IBCs with commuter vision and machine learning, more melanomas will be detected early and lives will be saved. Our long-term goal is to reduce melanoma related deaths and unnecessary biopsies by helping clinicians increase the predictive value of dermoscopy-based melanoma screening. We believe sensitivity and specificity of dermoscopy- based melanoma screening for non-expert screeners can be improved by assistive technology, which is highly desirable given the cost of false positives (patient stress and unnecessary biopsies) and the extremely high cost of false negatives (delayed melanoma treatment).