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

Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Song Wang, Chunwei Ma, George Shih, Yifan Peng, Mingchen Gao
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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).

胸片多标记分类的少射学习几何集成。
本文的目的是识别不常见的心胸疾病和胸片上的模式。在没有足够的标记训练样本的情况下,训练机器学习模型对具有多标签适应症的罕见病进行分类是具有挑战性的。我们的模型利用来自常见疾病的信息,并适应不太常见的提及。我们建议使用包含邻域成分分析损失的多标签少射学习(FSL)方案,使用分布校准和基于多标签分类损失的微调来生成额外的样本。我们利用了广泛采用的基于最近邻的FSL方案(如ProtoNet)是特征空间中的Voronoi图这一事实。在我们的方法中,由多标签方案生成的特征空间中的Voronoi图被组合到我们的几何DeepVoro多标签集成中。我们的实验证明了使用多标签集成在多标签少镜头分类中的改进性能(代码可在https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray上公开获得)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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