通过将图像文本信息与特征分离相结合实现多标签通用零镜头胸部 X 射线分类

Dwarikanath Mahapatra, Antonio Jimeno Yepes, Behzad Bozorgtabar, Sudipta Roy, Zongyuan Ge, Mauricio Reyes
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引用次数: 0

摘要

在基于完全监督学习的医学图像分类中,训练好的模型的鲁棒性会受到候选疾病类别范围的影响。广义零点学习(Generalized Zero Shot Learning,GZSL)旨在正确预测已见和未见的新类别。目前的 GZSL 方法主要侧重于单标签情况。然而,胸部 X 光片上标有多种疾病类别的情况很常见。我们提出了一种新颖的多模态多标签 GZSL 方法,该方法利用特征分解和多模态信息来综合未见类别的特征。疾病标签通过预训练的 BioBert 模型进行处理,以获得文本嵌入,用于创建不同标签之间相似性的编码字典。然后,我们使用分解特征和图聚合来学习第二份标签间相似性字典。随后的聚类步骤有助于确定每个类别的代表性向量。多模式多标签字典和类别代表向量用于指导特征合成步骤,这是我们管道中最重要的组成部分,用于生成真实的已见和未见类别的多标签疾病样本。根据在公开的美国国立卫生研究院(NIH)和 CheXpert 胸部 X 光数据集上进行的实验,我们的方法对多个竞争方法进行了基准测试,结果优于所有竞争方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Label Generalized Zero Shot Chest Xray Classification By Combining Image-Text Information With Feature Disentanglement.

In fully supervised learning-based medical image classification, the robustness of a trained model is influenced by its exposure to the range of candidate disease classes. Generalized Zero Shot Learning (GZSL) aims to correctly predict seen and novel unseen classes. Current GZSL approaches have focused mostly on the single-label case. However, it is common for chest X-rays to be labelled with multiple disease classes. We propose a novel multi-modal multi-label GZSL approach that leverages feature disentanglement andmulti-modal information to synthesize features of unseen classes. Disease labels are processed through a pre-trained BioBert model to obtain text embeddings that are used to create a dictionary encoding similarity among different labels. We then use disentangled features and graph aggregation to learn a second dictionary of inter-label similarities. A subsequent clustering step helps to identify representative vectors for each class. The multi-modal multi-label dictionaries and the class representative vectors are used to guide the feature synthesis step, which is the most important component of our pipeline, for generating realistic multi-label disease samples of seen and unseen classes. Our method is benchmarked against multiple competing methods and we outperform all of them based on experiments conducted on the publicly available NIH and CheXpert chest X-ray datasets.

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