Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.
Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.
复制标题
用于胸部 X 射线多标签分类的少样本学习几何集成。
DOI：
10.1007/978-3-031-17027-0_12
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Gao,Mingchen
中科院分区：
文献类型：
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作者：
Moukheiber,Dana;Mahindre,Saurabh;Moukheiber,Lama;Moukheiber,Mira;Wang,Song;Ma,Chunwei;Shih,George;Peng,Yifan;Gao,Mingchen
本文旨在识别不常见的心胸疾病和胸部 X 射线图像的模式。如果没有足够的标记训练样本，训练机器学习模型来对具有多标签适应症的罕见疾病进行分类是一项挑战。我们的模型利用常见疾病的信息，并适应不太常见的提及。我们建议使用多标签少样本学习（FSL）方案，包括邻域成分分析损失、使用分布校准生成额外样本以及基于多标签分类损失的微调。我们利用了这样一个事实：广泛采用的基于最近邻的 FSL 方案（例如 ProtoNet）是特征空间中的 Voronoi 图。在我们的方法中，从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的多标签少样本分类的改进性能（该代码可在 https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray 公开获取）。
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).