Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes

arXiv:2403.16499v1 Announce Type: new
Abstract: Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work …

abstract annotation arxiv cs.cv data image image data imaging medical networks planes pretraining prior self-supervised learning success supervised learning tasks tool transfer transfer learning type