Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Song Wang, Chunwei Ma, George Shih, Yifan Peng, Mingchen Gao
{"title":"Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.","authors":"Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Song Wang, Chunwei Ma, George Shih, Yifan Peng, Mingchen Gao","doi":"10.1007/978-3-031-17027-0_12","DOIUrl":null,"url":null,"abstract":"<p><p>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).</p>","PeriodicalId":93741,"journal":{"name":"Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore)","volume":"13567 ","pages":"112-122"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652771/pdf/nihms-1846293.pdf","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-17027-0_12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/9/16 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
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).