Enforcing Explainable Deep Few-Shot Learning to Analyze Plain Knee Radiographs

This work is the first to present an explainable deep few-shot learning model that can localize the knee joint area and segment the joint space in plain knee radiographs, using only a small number of manually annotated radiographs. The accuracy performance of the proposed method was thoroughly and experimentally evaluated using various image localization and segmentation measures, and it was compared to baseline models that utilized large-scale fully-annotated training datasets.