Unsupervised Local Discrimination for Medical Images

Contrastive learning has been proven effective to alleviate the high demand of expensive annotations in medical images analysis. Recent works are mainly based on instance-wise discrimination and learn global discriminative features; however, they cannot assist clinicians to deal with tiny anatomical structures, lesions, and tissues which are mainly distinguished by local similarities. In this work, we propose a general unsupervised framework to learn local discriminative features from medical images for models' initializations. Following the fact that images of the same body region should share similar anatomical structures, and pixels of the same structure should have similar semantic patterns, we design a neural network to construct a local discriminative embedding space where pixels with similar contexts are clustered and dissimilar pixels are dispersed. This network mainly contains two branches: an embedding branch to generate pixel-wise embeddings, and a clustering branch to gather pixels of the same structure together and generate segmentations. A region discriminative loss is proposed to optimize these two branches in a mutually beneficial pattern, making pixels clustered together by the clustering branch share similar embedded vectors and the trained model can measure pixel-wise similarity. When transferred to downstream tasks, the learnt feature extractor based on our framework shows better generalization ability, which outperforms those from extensive state-of-the-art methods and wins 11 out of all 12 downstream tasks in color fundus and chest X-ray. Furthermore, we utilize the pixel-wise embeddings to measure regional similarity and propose a shape-guided cross-modality segmentation framework and a center-sensitive one-shot landmark localization algorithm.Codes are released in

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