Semi-Supervised Disease Classification based on Limited Medical Image Data

arXiv:2405.04295v1 Announce Type: cross
Abstract: In recent years, significant progress has been made in the field of learning from positive and unlabeled examples (PU learning), particularly in the context of advancing image and text classification tasks. However, applying PU learning to semi-supervised disease classification remains a formidable challenge, primarily due to the limited availability of labeled medical images. In the realm of medical image-aided diagnosis algorithms, numerous theoretical and practical obstacles persist. The research on PU learning for medical image-assisted …

abstract arxiv challenge classification context cs.cv data disease eess.iv examples however image image data medical positive progress semi semi-supervised tasks text text classification type