Text-guided visual representation learning for medical image retrieval systems

Radiologists are now confronted with the difficulty of interpreting cross-sectional studies composed of thousands of images. In hospitals, all acquired imaging data are stored in a picture archiving and communication system (PACS). To take advantage of these masses of previously interpreted images, with the ultimate goal to facilitate the diagnosis of new cases, a promising approach would be to integrate a Content-Based Image Retrieval (CBIR) system into PACS. CBIR system performances are inherently limited by the features considered to represent the images. The current state of the art for the extraction of visual features relies on deep learning which requires a sufficient amount of annotated data to learn a generalizable model, such annotated data being rare and difficult to use in medical imaging. At the same time, PACS contain additional information such as radiological reports which supplement visual information carried by the images. We study here how such semantic information, hidden in these reports, can be used to supervise the learning of neuronal models to build a better visual representation of images. In this context our contribution is threefold. We first adapted a contrastive learning approach, which is usually used to learn representation from pairs of positive images in an unsupervised manner, to deal with in-domain medical data. Second, to train such a model to be robust and generalizable with a sufficient amount of data, we propose to re-employ the "dormant" medical imaging literature. Finally, the visual features and the deep models learned in this way, can be considered in CBIR systems as coarse-grained information which can then be fine-tuned in PACS, with more specific images depending on the applications. The obtained experimental result with state of the art contrastive learning methods highlight the interest of this approach.