Computer vision model that takes in an image of a receipt and outputs an itemization of all charges, each tagged with relevant item categories. To make this work properly, we needed to create a custom pipeline comprised of many deep neural networks. The first model performed object recognition, passed the output to an OCR model, then a model that we trained to understand receipt geometry, and a finally an NLP model that performed classification on the types of charges.
This system intakes 3 view mammogram scans of breast tissue and outputs the risk factors associated with the images. It improves on the current clinical accuracy of risk assessment by more than 10% in the majority demographic (caucasian women) and up to 60% improvement in the case of minority demographics. We adapted just released research into a production grade AI system and helped oversee and advise the process for technology acceptance by the FDA.