Brains behind the eyes: Interpreting Medical Images

现代医学成像仪可以生成详细的图像，但它们无法量化或理解这些图像。需要可以用来解释这些医学图像的计算模型，以理解“正常”的形状和功能，识别可能预示疾病发生的偏差，并量化速度和时间个体的变异性。这是“计算疾病视觉”的新兴领域，与计算机视觉非常相似，后者的智能算法是专门为分析和识别医学图像中存在的信号而设计的。开发眼睛后面的大脑，或智能计算机视觉算法，可以将原始成像数据转换为测量数据，可用于检测疾病的发作，自信地诊断疾病，或定量监控疾病进展，这是我研究计划的长期目标。*假设机器学习模型可以提取和理解医学成像数据中存在的比人类视觉分析更深层次的关系。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，以求从医学图像中识别疾病的总体长期目标。它们是：(1)设计标准和疾病特征的时空多尺度、多模式结构化表示；(2)设计用于开发用于疾病识别的新型分类器的常规、混合和深度模型。*建立理解人体解剖、形状和功能的整体模型，从而建立识别和量化疾病的模型是一个巨大的挑战。在人群的正常状态中存在固有的可变性，因此与正常的可变性相比，感兴趣的信号可能是微妙和微弱的。标记疾病发生的信号存在并且具有多个尺度，并且通常以结构变化的相对术语来描述，因此需要能够捕捉解剖结构内跨尺度和位置的多级别交互的语义表示。通常，单一模式可能只捕捉到部分变化，例如，在视网膜中，视网膜层几何结构的变化可能较弱，但除了视网膜血管系统的变化外，还提供了更强的判别信号来识别量化疾病。我们建议开发对传统形状模型的新扩展，直接作用于原始医学图像的深层结构模型，以及结合传统模型和深层结构模型的混合模型，用于从医学图像中自动识别疾病。**

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. **