Learning Few-Shot Chest X-Ray Diagnosis Using Images From The Published Scientific Literature

Abstract

A trained radiologist may learn the visual presentation of a new disease by looking at a few relevant image examples in research articles. However, training a machine learning model in such a manner is an arduous task not only due to the small number of labeled training images but also for the low resolution of such images. We design a few-shot learning method that can diagnose new diseases from chest x-rays utilizing only a few relevant labeled x-ray images from the published literature. Our method uses prior knowledge about other diseases for feature extraction from x-rays of new diseases. We formulate a classifier that is initially trained with a few labeled feature vectors corresponding to low-resolution images from the PubMed Central. The classifier is subsequently re-trained using unlabeled feature vectors corresponding to high-resolution x-ray images. Experiments on publicly available datasets show the superiority of the proposed method to several state-of-the-art few-shot learning techniques for chest x-ray diagnosis.