Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.

TitleFew-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.
Publication TypeJournal Article
Year of Publication2022
AuthorsMoukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M
JournalData Augment Label Imperfections (2022)
Date Published2022 Sep

This paper aims to identify uncommon cardiothoracic diseases and patterns on chest X-ray images. Training a machine learning model to classify rare diseases with multi-label indications is challenging without sufficient labeled training samples. Our model leverages the information from common diseases and adapts to perform on less common mentions. We propose to use multi-label few-shot learning (FSL) schemes including neighborhood component analysis loss, generating additional samples using distribution calibration and fine-tuning based on multi-label classification loss. We utilize the fact that the widely adopted nearest neighbor-based FSL schemes like ProtoNet are Voronoi diagrams in feature space. In our method, the Voronoi diagrams in the features space generated from multi-label schemes are combined into our geometric DeepVoro Multi-label ensemble. The improved performance in multi-label few-shot classification using the multi-label ensemble is demonstrated in our experiments (The code is publicly available at https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray).

Alternate JournalData Augment Label Imperfections (2022)
PubMed ID36383493
PubMed Central IDPMC9652771
Grant ListR00 LM013001 / LM / NLM NIH HHS / United States