Unsupervised Local Discrimination for Medical Images
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Contrastive representation learning is an effective unsupervised method to alleviate the demand for expensive annotated data in medical image processing. Recent work mainly based on instance-wise discrimination to learn global features, while neglect local details, which limit their application in processing tiny anatomical structures, tissues and lesions. Therefore, we aim to propose a universal local discrmination framework to learn local discriminative features to effectively initialize medical models, meanwhile, we systematacially investigate its practical medical applications. Specifically, based on the common property of intra-modality structure similarity, i.e. similar structures are shared among the same modality images, a systematic local feature learning framework is proposed. Instead of making instance-wise comparisons based on global embedding, our method makes pixel-wise embedding and focuses on measuring similarity among patches and regions. The finer contrastive rule makes the learnt representation more generalized for segmentation tasks and outperform extensive state-of-the-art methods by wining 11 out of all 12 downstream tasks in color fundus and chest X-ray. Furthermore, based on the property of inter-modality shape similarity, i.e. structures may share similar shape although in different medical modalities, we joint across-modality shape prior into region discrimination to realize unsupervised segmentation. It shows the feaibility of segmenting target only based on shape description from other modalities and inner pattern similarity provided by region discrimination. Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization, this is an effective application for patch discrimination.
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