标题： 基于 COVID-19 CT 影像和未校正报告的零样本多标签分类实证分析 显示英文标题

标题： An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports

摘要： 疫情导致由于增加了医学检查，产生了大量非结构化数据，包括放射科报告。 以往关于冠状病毒自动诊断的研究主要集中在X光图像上，尽管与计算机断层扫描（CT）相比，其精度较低。 在本工作中，我们利用医院的非结构化数据，并利用CT扫描提供的细粒度细节，基于对比视觉语言学习进行零样本多标签分类。 与人类专家合作，我们研究了多种零样本模型的有效性，这些模型有助于放射科医生检测肺栓塞并识别如磨玻璃混浊和实变等复杂的肺部细节。 我们的实证分析概述了迄今为止在医学多模态预训练文献中被忽视的此类细粒度任务的可能解决方案。 我们的研究通过解决与非结构化数据和细粒度多标签分类相关的挑战，有望为医学图像分析领域带来未来的进展。 显示英文摘要

摘要： The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations. Previous research on automated diagnosis of COVID-19 primarily focuses on X-ray images, despite their lower precision compared to computed tomography (CT) scans. In this work, we leverage unstructured data from a hospital and harness the fine-grained details offered by CT scans to perform zero-shot multi-label classification based on contrastive visual language learning. In collaboration with human experts, we investigate the effectiveness of multiple zero-shot models that aid radiologists in detecting pulmonary embolisms and identifying intricate lung details like ground glass opacities and consolidations. Our empirical analysis provides an overview of the possible solutions to target such fine-grained tasks, so far overlooked in the medical multimodal pretraining literature. Our investigation promises future advancements in the medical image analysis community by addressing some challenges associated with unstructured data and fine-grained multi-label classification.