Unsupervised, Semi-supervised, Weakly-supervised Learning in Biomedical Image Analysis

Abstract

Supervised learning such as deep learning has applied to various tasks and achieved high accuracy in biomedical image analysis. However, it is required to prepare sufficient labeled data to learn discriminative features for robust recognition. It often requires significant effort by biomedical experts to annotate images for various objects, imaging modality and types of disease. In this paper, I introduce current works about unsupervised, semi-supervised, and weakly-supervised learning, which enables to reduce the annotation costs. I then introduce our researches on these learning problems.