ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases

By Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers. 2019

1,263 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Evaluation & Benchmarks

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Citations in the corpus

Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020, methodology): “Nevertheless, this rule-based label extraction approach has two limitations: 1) the rules are often inaccurate and limited to a few major categories (Wang et al., 2017), leading to very inefficient use of the textual report data; 2) these rules are often domain-specific and sensitive to the style of”
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (2021, methodology): “While the first four datasets contains natural images, ChestXRay8 consists of medical images.”
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first (2020, background): “National Institutes of Health (NIH) has a largescale database of approximately 110,000 chest X-ray images, named ChestX-ray8 database [18].”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, background): “The largest open public set of chest x-rays, ‘‘ChestX-ray8’’, composed of 108,948 frontal-view x-rays from 32,717 patients, was derived from a combination of keyword search and removal of negation and uncertainty [45].”
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images (2019, background): “The former type often encounters the issues of multi-object classification and training data imbalance [36], for which binary cross entropy (BCE) loss [5] as well as class-balancing techniques [36] [15] were explored.”
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing (2022, methodology): “Such data is collected routinely during clinical practice, and common examples are X-ray images [19,33,76] or computed tomography (CT) scans [9,19,21,72] paired with reports written by medical experts.”
Joint Learning of Localized Representations from Medical Images and Reports (2021, methodology): “We download the reports from MIMIC-CXR7 and extract the text from the Findings and Impression sections.”
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis (2020, methodology): “ChestMNIST [10] Chest X-ray Multi-Label(14) Binary-Class (2) 78,468 11,219 22,433”
Deep convolutional neural network based medical image classification for disease diagnosis (2019, background): “Moreover, in other research, it also found that CNN based system can be trained from big chest X-ray (CXR) film dataset and state-of-art with high accuracy and sensitivity results on their dataset, like Stanford Normal Radiology Diagnostic Dataset containing more than 400,000 CXR and a new CXR datab”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “[158] 2021 Facilitate the integration of any medical data from any data owner worldwide without violating privacy laws Cross-silo Decentralized Real Custom model Custom aggregator Asynchronous Scheduling PBMC transcriptome [159] Chest X-ray [160]”