医学成像分析在过去需要人工检查员的灵活性和对非结构化场景做出定性决策的能力。考虑到背景的混乱或图像质量问题，精确定位感兴趣的物体或区域可能不但耗时而且较困难。自动化系统必须能成功地识别感兴趣的区域，同时忽略不相关的功能特征。现在，深度学习式图像分析可以自动搜索放射 X 光片、超声和核磁共振中的生物异常。
Whether searching for a specific anomaly or any deviation from the body’s normal appearance, Cognex ViDi combines the flexibility of a human inspector with the speed and robustness of a computerized system. The ViDi Blue-Locate tool locates the region of interest (e.g. a certain organ), despite the visually confusing and poorly contrasted background, by learning the distinguishing features of that area. The ViDi Red-Analyze tool develops a reference model of an organ’s normal appearance, as well as specific types of anomalies, based on training on a set of sample images. Any anomalies which digress from the normal physiology of the targeted zone are flagged as defects for a CAD computer-aided diagnosis by an expert radiologist.