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Self-supervised learning provides an opportunity to explore unlabeled chestX-rays and their associated free-text reports accumulated in clinical routinewithout manual supervision. This paper proposes a Joint Image TextRepresentation Learning Network (JoImTeRNet) for pre-training on chest X-rayimages and their radiology reports. The model was pre-trained on both theglobal image-sentence level and the local image region-word level forvisual-textual matching. Both are bidirectionally constrained on Cross-Entropybased and ranking-based Triplet Matching Losses. The region-word matching iscalculated using the attention mechanism without direct supervision about theirmapping. The pre-trained multi-modal representation learning paves the way fordownstream tasks concerning image and/or text encoding. We demonstrate therepresentation learning quality by cross-modality retrievals and multi-labelclassifications on two datasets: OpenI-IU and MIMIC-CXR

Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment