Brains behind the eyes: Interpreting Medical Images

现代医学成像仪可以生成详细的图像，但他们无法量化或理解这些图像。需要能够用于解释这些医学图像的计算模型来理解“正常”的形状和功能，识别可能预示疾病发病的偏差，并量化个体变异的速度和时间。这是“计算疾病视觉”的新兴领域，与计算机视觉有密切的相似之处，其中智能算法专门用于分析和识别医学图像中存在的信号。开发“眼睛后面”的大脑，或者智能计算机视觉算法，可以将原始成像数据转换为可用于检测疾病发作、自信地诊断疾病或定量监测疾病进展的测量值，这是我研究计划的长期目标。******假设是，机器学习模型可以提取和理解医学成像数据中存在的更深层次的关系，而不是人类的视觉分析。因此，这种计算视觉算法无疑是临床图像解释的未来。我们提出了两个具体的短期目标，以实现从医学图像中识别疾病的总体长期追求。这包括：(1)设计规范和疾病特征的时空多尺度、多模态结构化表征；(2)设计常规、混合和深度模型，用于开发新的疾病识别分类器。******建立理解人体解剖、形状和功能的整体模型，从而建立疾病识别和量化模型，目前面临重大挑战。在人群的规范状态中存在固有的可变性，因此与正常的可变性相比，兴趣信号可能是微妙而微弱的。标志疾病发病的信号存在于多个尺度上，并且通常以结构变化的相对术语来描述，因此需要一种语义表示，可以捕获解剖结构中跨尺度和位置的多层相互作用。通常，单一模式可能只捕获部分变化，例如，在视网膜中，视网膜层几何形状的变化可能较弱，但除了视网膜脉管系统的变化外，还提供更强的判别信号，以识别量化疾病。在本提案中，我们建议开发传统形状模型的新扩展，直接作用于原始医学图像的深度结构模型，以及结合传统和深度结构模型优点的混合模型，用于医学图像的自动疾病识别。**

Modern medical imagers generate detailed images but they cannot quantify or understand' these images. Computational models that can be used to interpret these medical images are needed to understand “normal” shape and function and identify deviations that may signal the onset of disease and to quantify the pace and temporal individual variability. This is the emerging field of “computational disease vision,” with close parallels to computer vision, where intelligent algorithms are designed specifically for analyzing and recognizing the signals present in medical images. Developing the brains' behind the eyes', or the intelligent computer vision algorithms that can convert raw imaging data into measurements that can be used to detect the onset of disease, diagnose a disease with confidence, or to quantitatively monitor disease progression is the long-term goal of my research program. ******The hypothesis is that machine learning models can extract and understand deeper relationships present in medical imaging data than are possible with human visual analysis. Hence, such computational vision algorithms are undoubtedly the future for clinical image interpretation. We propose two specific short-term goals towards the overarching long term quest for disease recognition from medical images. These are: (1) design of spatio-temporal multi-scale, multi-modal structured representations of normative and disease signatures, and (2) design of conventional, mixed- and deep-models for developing novel classifiers for disease recognition. ******Building holistic models for understanding human anatomy, shape and function, and thereby, models for recognition and quantification of disease present significant challenges. There is inherent variability across normative state in the population, and hence the signal of interest can be subtle and weak as compared to normal variability. The signals that mark the onset of disease exist and multiple scales, and are often described in relative terms of a configuration change, and hence require a semantic representation that can capture multiple levels of interaction across scales and locations within the anatomy. Often, a single modality may only capture part of the changes, for example, in the retina, the changes in retina layer geometry may be weaker but in addition to changes in the retina vasculature, provide a stronger discriminant signal to recognise the quantify disease. We propose to develop novel extensions to conventional shape models, deep-structured models that act on raw medical images directly, as well as mixed models combining the best of both conventional and deep-structured models for automated disease recognition from medical images in this proposal. **