all AI news
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier. (arXiv:2112.09356v2 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
Self-supervised learning (SSL) opens up huge opportunities for medical image
analysis that is well known for its lack of annotations. However, aggregating
massive (unlabeled) 3D medical images like computerized tomography (CT) remains
challenging due to its high imaging cost and privacy restrictions. In this
paper, we advocate bringing a wealth of 2D images like chest X-rays as
compensation for the lack of 3D data, aiming to build a universal medical
self-supervised representation learning framework, called UniMiSS. The
following problem is …
arxiv breaking cv dimensionality learning medical self-supervised learning supervised learning