ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

arXiv:2306.10535v2 Announce Type: replace-cross
Abstract: Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many …

abstract arxiv bag class cs.cv cs.lg eess.iv imaging instance instances medical medical imaging mil multiple predictions type weakly-supervised