醫學成像分析在過去需要人工檢查員的靈活性和對非結構化場景做出定性決策的能力。考慮到背景的混亂或圖像質量問題，精確定位感興趣的物體或區域可能不但耗時而且較困難。自動化系統必須能成功地識別感興趣的區域，同時忽略不相關的功能特征。現在，深度學習式圖像分析可以自動搜索放射 X 光片、超聲和核磁共振中的生物異常。
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.