Abstract: Systems and methods can perform self-supervised machine learning for improved medical image analysis. As one example, self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled medical images from the target domain of interest, followed by fine-tuning on labeled medical images from the target domain significantly improves the accuracy of medical image classifiers such as, for example diagnostic models. Another example aspect of the present disclosure is directed to a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple different medical images that share one or more attributes (e.g., multiple images that depict the same underlying pathology and/or the same patient) to construct more informative positive pairs for self-supervised learning.

Abstract: Methods and systems for high-resolution image inpainting are disclosed. An original high-resolution image to be inpainted is obtained, as well as an inpainting mask indicating an inside-mask area to be inpainted. The original high-resolution image is down-sampled to obtain a low-resolution image to be inpainted. Using a trained inpainting generator, a low-resolution inpainted image and a set of attention scores are generated from the low-resolution image. The attention scores represent the similarity between inside-mask regions and outside-mask regions. A high-frequency residual image is computed from the original high-resolution image. An aggregated high-frequency residual image is generated using the attention scores, including high-frequency residual information for the inside-mask area. A high-resolution inpainted image is outputted by combining the aggregated high-frequency residual image and a low-frequency inpainted image generated from the low-resolution inpainted image.

Abstract: Methods and systems for high-resolution image inpainting are disclosed. An original high-resolution image to be inpainted is obtained, as well as an inpainting mask indicating an inside-mask area to be inpainted. The original high-resolution image is down-sampled to obtain a low-resolution image to be inpainted. Using a trained inpainting generator, a low-resolution inpainted image and a set of attention scores are generated from the low-resolution image. The attention scores represent the similarity between inside-mask regions and outside-mask regions. A high-frequency residual image is computed from the original high-resolution image. An aggregated high-frequency residual image is generated using the attention scores, including high-frequency residual information for the inside-mask area. A high-resolution inpainted image is outputted by combining the aggregated high-frequency residual image and a low-frequency inpainted image generated from the low-resolution inpainted image.