Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（生物医学网络生物工程研究中心）；Children’s National Hospital（儿童国家医院）；GWU School of Medicine（乔治华盛顿大学医学院）
Daniel Capellán-Martín, Abhijeet Parida, Juan J. Gómez-Valverde, Ramon Sanchez-Jacob, Pooneh Roshanitabrizi, Marius G. Linguraru, María J. Ledesma-Carbayo, Syed M. Anwar
备注：
5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024
发表信息：
21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece
Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce.
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（生物工程、生物材料与纳米医学网络研究中心）；Children’s National Hospital（国家儿童医院）；George Washington University School of Medicine（乔治华盛顿大学医学院）；ISGlobal（巴塞罗那全球健康研究所）；Hospital Clínic（巴塞罗那临床医院）；Universitat de Barcelona（巴塞罗那大学）；Stellenbosch University（斯泰伦博斯大学）
SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
Federal University of Agriculture Abeokuta, Nigeria（尼日利亚阿贝奥库塔联邦农业大学）；University of Lagos, Nigeria（尼日利亚拉各斯大学）；University of Nigeria, Nsukka（尼日利亚恩苏克卡大学）；University of Benin, Nigeria（尼日利亚贝宁大学）；Olabisi Onabanjo University, Nigeria（尼日利亚奥拉比·奥纳班乔大学）；Federal University of Technology, Minna, Nigeria（尼日利亚米纳联邦技术大学）；McGill University, Canada（加拿大麦吉尔大学）；AGH University of Krakow, Poland（波兰克拉科夫AGH大学）
Deep Learning for Automated Screening of Tuberculosis from Indian Chest X-rays: Analysis and Update
Indian Institute of Technology Delhi（印度德里理工学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College（基督教医学院）
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Bangabandhu Sheikh Mujib Medical University（邦班杜·谢赫·穆吉布医科大学）；Hamad General Hospital（哈马德综合医院）；Weill Cornell Medicine – Qatar（威尔康奈尔医学院卡塔尔分校）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；North South University（北南大学）；College of Engineering, Qatar University（卡塔尔大学工程学院）
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Medical Imaging Center, Vingroup Big Data Institute（Vingroup大数据研究院医学影像中心）；College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Saint Louis University（圣路易斯大学）
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
Radboud University Medical Center（拉德堡德大学医学中心）；Community Health Solutions（社区健康解决方案公司）；Interactive Research and Development（互动研究与发展机构）；The Indus Health Network（印度河健康网络）；Thirona（Thirona公司）；Universal Delft Ltd.（代尔夫特环球有限公司）
TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（DarwinAI公司）；Niagara Health（尼亚加拉健康中心）；McMaster University（麦克马斯特大学）；Thunder Bay Regional Health Sciences Centre（桑德贝地区健康科学中心）；Northern Ontario School of Medicine（北安大略医学院）
Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting
Stop TB Partnership（遏制结核病伙伴关系）；International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b)（孟加拉国国际腹泻病研究中心）
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Google Health（谷歌健康）；Advanced Clinical（先进临床）；Apollo Radiology International（阿波罗国际放射学）；Center of Infectious Disease Research in Zambia（赞比亚传染病研究中心）；Sibanye Stillwater（斯班耶-斯蒂尔沃特）；Clickmedix
Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology Delhi（印度理工学院德里分校巴蒂电信技术与管理学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College, Vellore（韦洛尔基督教医学院）
Cross-dataset transportability of pediatric chest X-ray deep learning across three countries: discrimination, calibration, operating-point failure, and limited-label recovery
American International University-Bangladesh (AIUB)（美国国际大学孟加拉分校）
Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Hamedan University of Technology（哈马丹理工大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Stanford University（斯坦福大学）
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System（儿童国家卫生系统谢赫扎耶德儿科外科创新研究所）；Children’s National Health System（儿童国家卫生系统）；San Francisco State University（旧金山州立大学）；School of Medicine and Health Sciences, George Washington University（乔治华盛顿大学医学与健康科学学院）
已提出将计算机辅助诊断（CAD）技术用于成人队列的胸片（CXR）肺野分割，但很少用于儿科受试者。统计形状模型（SSM），最先进的基于CXR的肺野分割方法的主力，不能有效地适应在儿科发育阶段的肺野的形状变化。我们工作的主要贡献是：（1）来自CXR的通用肺野分割框架，适应成人和儿科队列的大形状变化;（2）深度表示学习检测机制\emph{ensemble space learning}，用于鲁棒的对象定位;以及（3）\emph{marginal shape deep learning}，用于形状变形参数估计。与传统SSM的迭代方法不同，所提出的形状学习机制将参数空间转换为边缘子空间，这些边缘子空间使用递归表示学习机制有效地求解。此外，我们的方法是第一个在基于CXR的肺分割中包括具有挑战性的心脏后区域，以进行准确的肺活量估计。该框架在3个月至89岁的患者的668个CXR上进行评估。我们得到平均Dice相似系数为$0.96\pm0.03$（包括心脏后区域）。对于一个给定的精度，所提出的方法也被发现比传统的基于SSM的迭代分割方法更快。所提出的通用框架的计算简单性可以类似地应用于其他可变形对象的快速分割。
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
Department of Computer Science, College of Science, Charmo University（查莫大学计算机科学系，科学学院）；Department of Software Engineering, University of Raparin（拉帕林大学软件工程系）
GAN-based disentanglement learning for chest X-ray rib suppression
Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute (NKI)（荷兰癌症研究所）；Z 2 Sky Technologies Inc.（Z 2 Sky 技术公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Lab for AI in Medicine, RWTH Aachen University（人工智能医学实验室，亚琛工业大学）；Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen（诊断与介入放射学部门，亚琛大学医院）；Department of Urology, Stanford University（泌尿科，斯坦福大学）；Department of Radiology, Stanford University（放射科，斯坦福大学）；Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, University Hospital RWTH Aachen（计算基因组研究所，计算生物医学联合研究中心，亚琛大学医院）；Else Kroener Fresenius Center for Digital Health, Technical University Dresden（数字健康中心，德累斯顿技术大学）；Department of Medicine I, University Hospital Dresden（第一医学部，德累斯顿大学医院）；National Center for Tumor Diseases (NCT), University Hospital Heidelberg（肿瘤疾病国家中心（NCT），海德堡大学医院）
