Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center（影像生物标志物与计算机辅助诊断实验室，放射学与成像科学系，国家卫生研究院临床中心）
Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have also been developed for report labeling but lack adaptability, similar to rule-based systems. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on CXR reports. MAPLEZ extracts not only binary labels indicating the presence or absence of a finding but also the location, severity, and radiologists' uncertainty about the finding. Over eight abnormalities from five test sets, we show that our method can extract these annotations with an increase of 3.6 percentage points (pp) in macro F1 score for categorical presence annotations and more than 20 pp increase in F1 score for the location annotations over competing labelers. Additionally, using the combination of improved annotations and multi-type annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1.1 pp in AUROC over models trained with annotations from the best alternative approach. We share code and annotations.
Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与影像研究所）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）
Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
Department of Electrical and Computer Engineering（电子与计算机工程系）；Sungkyunkwan University（成均馆大学）；Center for Neuroscience Imaging Research（神经科学成像研究中心）；Institute for Basic Science（基础科学研究所）；Department of Artificial Intelligence（人工智能系）
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Delft University of Technology（代尔夫特理工大学）；Erasmus Medical Center - Sophia Children’s Hospital（伊拉斯谟医学中心-索菲亚儿童医院）；Erasmus Medical Center（伊拉斯谟医学中心）；University of Copenhagen（哥本哈根大学）
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
Eindhoven University of Technology（埃因霍温理工大学）；Erasmus MC - University Medical Center Rotterdam（伊拉斯谟医学中心-鹿特丹大学医学中心）；Erasmus MC - Sophia Children’s Hospital（伊拉斯谟医学中心-索菲亚儿童医院）；University of Copenhagen（哥本哈根大学）
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Department of Radiology, Northwestern University, Chicago, IL, USA（西北大学放射科）；Data Science Institute, Vanderbilt University, Nashville, TN, USA（范德比尔特大学数据科学研究院）；Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二医院骨科）；Second Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二临床医学院）；School of Computer Science, University of Sydney, Sydney, NSW, Australia（悉尼大学计算机科学学院）
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；Imsight AI Research Lab（Imsight人工智能研究实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所（sinc(i) CONICET-UNL））；Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）
PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism
1 ,5Department of Computer Science \& Engineering,\ University of Engineering \& Technology, Rajshahi-6204, Bangladesh；2 Department of Electrical；Electronic Engineering,\ University of Engineering；Bachelor of Surgery,\ Suhrawardy Medical College , Sher-E-Bangla Nagar, Dhaka-1207；6 Department of Biomedical Engineering,\ University of Engineering
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）；Peter Munk Cardiac Centre, University Health Network（多伦多大学健康网络彼得·芒克心脏中心）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
本文提出 EVA-X 自监督胸部 X 光基础模型，可同时学习语义与几何表示，在 20 多种疾病和 11 项检测任务上取得领先性能，并降低标注需求。
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Daimler TSS（戴姆勒TSS公司）；University Hospital Heidelberg（海德堡大学医院）；German Cancer Research Center（德国癌症研究中心）；University Clinic Essen（埃森大学医院）
A foundation model for generalizable disease diagnosis in chest X-ray images
Centre for Perceptual and Interactive Intelligence, the Chinese University of Hong Kong（香港中文大学感知与交互智能中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Department of Electronic Engineering, the Chinese University of Hong Kong（香港中文大学电子工程系）
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；German Cancer Research Center Heidelberg（海德堡德国癌症研究中心）
When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI
Medical Imaging and AI in Healthcare Research Group, Biosciences Center, SURA（SURA生物科学中心医疗成像与医疗保健人工智能研究组）；Universidad de Antioquia（安蒂奥基亚大学）；IBM Research（IBM研究院）；Neuroradiology Institute, Universitätsklinikum Erlangen（埃尔兰根大学医院神经放射学研究所）
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；Imsight Technology, Co., Ltd.（深圳视见科技有限公司）；The University of Hong Kong（香港大学）；Shenzhen People's Hospital（深圳市人民医院）；Guangdong Provincial People's Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）；Queen Marry Hospital（玛丽医院）；Hospital Authority（医院管理局）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures
Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国家技术大学）
