cxr - l2024: MICCAI对胸部x线长尾、多标签和零镜头疾病分类的挑战

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mingquan Lin , Gregory Holste , Song Wang , Yiliang Zhou , Yishu Wei , Imon Banerjee , Pengyi Chen , Tianjie Dai , Yuexi Du , Nicha C. Dvornek , Yuyan Ge , Zuwei Guo , Shouhei Hanaoka , Dongkyun Kim , Pablo Messina , Yang Lu , Denis Parra , Donghyun Son , Álvaro Soto , Aisha Urooj , Yifan Peng
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引用次数: 0

摘要

CXR- lt系列是一项社区驱动的倡议,旨在通过胸部x光片(CXR)加强肺部疾病分类。它解决了开放式长尾肺疾病分类的挑战,并提高了最先进技术的可测量性。第一个事件是CXR- lt 2023,旨在通过为模型开发提供高质量的基准CXR数据并进行全面评估以确定影响肺部疾病分类性能的持续问题来实现这些目标。基于CXR-LT 2023的成功,CXR-LT 2024将数据集扩展到377,110个胸部x射线(cxr)和45个疾病标签,包括19个新的罕见疾病发现。它还引入了对零射击学习的新关注,以解决之前事件中确定的限制。具体而言,CXR-LT 2024具有三个任务:(i)在大型噪声测试集上进行长尾分类,(ii)在手动注释的“金标准”子集上进行长尾分类,以及(iii)对五种以前未见过的疾病发现进行零概率推广。本文概述了CXR-LT 2024,详细介绍了数据管理过程和整合最先进的解决方案,包括使用多模态模型进行罕见疾病检测,先进的生成方法处理噪声标签,以及针对未见疾病的零采样学习策略。此外,扩展的数据集增强了疾病覆盖范围,以更好地代表现实世界的临床环境,为未来的研究提供了宝贵的资源。通过综合参与团队的见解和创新,我们的目标是促进临床实用和可推广的胸片诊断模型的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance. Building on the success of CXR-LT 2023, the CXR-LT 2024 expands the dataset to 377,110 chest X-rays (CXRs) and 45 disease labels, including 19 new rare disease findings. It also introduces a new focus on zero-shot learning to address limitations identified in the previous event. Specifically, CXR-LT 2024 features three tasks: (i) long-tailed classification on a large, noisy test set, (ii) long-tailed classification on a manually annotated “gold standard” subset, and (iii) zero-shot generalization to five previously unseen disease findings. This paper provides an overview of CXR-LT 2024, detailing the data curation process and consolidating state-of-the-art solutions, including the use of multimodal models for rare disease detection, advanced generative approaches to handle noisy labels, and zero-shot learning strategies for unseen diseases. Additionally, the expanded dataset enhances disease coverage to better represent real-world clinical settings, offering a valuable resource for future research. By synthesizing the insights and innovations of participating teams, we aim to advance the development of clinically realistic and generalizable diagnostic models for chest radiography.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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