MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Johnson, Alistair E. W.; Pollard, Tom; Berkowitz, Scott A.; Greenbaum, Nathaniel R.; Lungren, Matthew P.; Deng, Chih-Ying; Mark, Roger G.; Horng, Steven

doi:10.1038/s41597-019-0322-0

Cited by 884 publications

(516 citation statements)

References 31 publications

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“…The large dataset of chest X-rays CheXpert provides another comprehensive source for pre-training the models [ 16 ], and 2.44% of the CheXpert images are from the pneumonia patients. The third dataset MIMIC-CXR covers 297 class labels for its chest X-ray images, and 6.9% of its images are from pneumonia patients [ 17 ].This study serves as the proof-of-principle experiment for the two-stage transfer learning strategy. So these existing datasets are not evaluated in this study.…”

Section: Methodsmentioning

confidence: 99%

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

Zhang

Guo

Sun

et al. 2020

Interdiscip Sci Comput Life Sci

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Section: Methodsmentioning

confidence: 99%

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

Zhang

Guo

Sun

et al. 2020

Interdiscip Sci Comput Life Sci

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“…The data set for model training (hereafter, the modeling data set) was assembled from 2 hospital sources: MIMIC-4, 19 data set were used owing to available reports, the NIH data set was randomly sampled for report generation, from which only a subset of 11 692 images could be manually reread.…”

Section: Assembling the Data Sets For Model Trainingmentioning

confidence: 99%

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

et al. 2020

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IMPORTANCE Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. OBJECTIVE To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set

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“…Recently, thanks to the increased availability of large scale, high-quality labeled datasets [15,14,16] and high-performing deep network architectures [17,18,19,20], deep learning-based approaches have been able to reach, even outperform expert-level performance for many medical image interpretation tasks [21,22,23,24]. Most successful applications of deep neural networks in medical imaging rely on CNNs, which were introduced in 1998 by LeCun et al [25] and revolutionized in 2012 by Krizhevsky et al [26].…”

Section: Deep Learning In Medical Imagingmentioning

confidence: 99%

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Pham¹,

Le²,

Tran³

et al. 2019

Preprint

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Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem by training state-of-the-art CNNs that exploit dependencies among abnormality labels. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. This is the highest AUC score yet reported to date. The proposed method is also evaluated on the independent test set of the CheXpert competition, which is composed of 500 CXR studies annotated by a panel of 5 experienced radiologists. The performance is on average better than 2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of writing this paper.

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MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Cited by 884 publications

References 31 publications

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

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