BRAX, Brazilian labeled chest x-ray dataset

Reis, Eduardo Pontes; Paiva, Joselisa Péres Queiroz de; Silva, Maria C B da; Ribeiro, Guilherme; Paiva, Victor F; Bulgarelli, Lucas; Lee, Henrique Min Ho; Santos, Paulo V; Brito, Vanessa Mizubuti; Amaral, Lucas Tadashi Wada; Beraldo, Gabriel Laverdi; Filho, Jorge N Haidar; Teles, Gustavo B S; Szarf, Gilberto; Pollard, Tom; Johnson, Alistair E. W.; Celi, Leo Anthony; Amaro, Edson

doi:10.1038/s41597-022-01608-8

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…Our label extraction algorithm was successful in identifying corresponding labels in DS 1 across all classes. The results are in line with other methods proposed in the literature 12 16 18 . Missed classifications were mostly due to missing phrases.…”

Section: Discussionsupporting

confidence: 91%

“…To minimize development time, the CheXpert labeler was used to annotate the MIMIC-CXR data set as well. Moreover, this labeler has been adapted and ported to process reports in other languages, such as Brazilian [18] and Vietnamese [19]. The process of labeling consists of three stages: In the first stage, mention extraction, the labeler scans the report for phrases typical for a class as defined in class-specific lists.…”

Section: Introductionmentioning

confidence: 99%

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German CheXpert Chest X-ray Radiology Report Labeler

Wollek,

Hyska,

Sedlmeyr

et al. 2024

Rofo

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Purpose The aim of this study was to develop an algorithm to automatically extract annotations from German thoracic radiology reports to train deep learning-based chest X-ray classification models. Materials and Methods An automatic label extraction model for German thoracic radiology reports was designed based on the CheXpert architecture. The algorithm can extract labels for twelve common chest pathologies, the presence of support devices, and “no finding”. For iterative improvements and to generate a ground truth, a web-based multi-reader annotation interface was created. With the proposed annotation interface, a radiologist annotated 1086 retrospectively collected radiology reports from 2020–2021 (data set 1). The effect of automatically extracted labels on chest radiograph classification performance was evaluated on an additional, in-house pneumothorax data set (data set 2), containing 6434 chest radiographs with corresponding reports, by comparing a DenseNet-121 model trained on extracted labels from the associated reports, image-based pneumothorax labels, and publicly available data, respectively. Results Comparing automated to manual labeling on data set 1: “mention extraction” class-wise F1 scores ranged from 0.8 to 0.995, the “negation detection” F1 scores from 0.624 to 0.981, and F1 scores for “uncertainty detection” from 0.353 to 0.725. Extracted pneumothorax labels on data set 2 had a sensitivity of 0.997 [95 % CI: 0.994, 0.999] and specificity of 0.991 [95 % CI: 0.988, 0.994]. The model trained on publicly available data achieved an area under the receiver operating curve (AUC) for pneumothorax classification of 0.728 [95 % CI: 0.694, 0.760], while the models trained on automatically extracted labels and on manual annotations achieved values of 0.858 [95 % CI: 0.832, 0.882] and 0.934 [95 % CI: 0.918, 0.949], respectively. Conclusion Automatic label extraction from German thoracic radiology reports is a promising substitute for manual labeling. By reducing the time required for data annotation, larger training data sets can be created, resulting in improved overall modeling performance. Our results demonstrated that a pneumothorax classifier trained on automatically extracted labels strongly outperformed the model trained on publicly available data, without the need for additional annotation time and performed competitively compared to manually labeled data. Key points:

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

German CheXpert Chest X-ray Radiology Report Labeler

Wollek,

Hyska,

Sedlmeyr

et al. 2024

Rofo

View full text Add to dashboard Cite

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“…To assess whether models can generalize across clinical distributions, we chose a wide variety of downstream CXR datasets that we used to finetune and validate our models. These datasets came from diverse sites, including Brazil 23 , China 24,25 , United States ,25,,26,27,28,29,30,31 , Spain 32 , Japan 33 , Vietnam 34 and other countries in Eastern Europe and Central Asia. 35 They also addressed a large set of tasks, such as lung nodule detection, line and tube placement, edema severity classification, pediatric pneumonia detection, pneumothorax detection and multi-class differential diagnosis; some datasets address wide-ranging detection tasks for multiple pathologies.…”

Section: Resultsmentioning

confidence: 99%

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

Gulati

Banerjee

et al. 2022

Preprint

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Chest X-rays (CXRs) are a rich source of information for physicians, essential for disease diagnosis and treatment selection. Recent deep learning models aim to alleviate strain on medical resources and improve patient care by automating the detection of diseases from CXRs. However, shortages of labeled CXRs can pose a serious challenge when training models. Currently, models are generally pretrained on ImageNet, but they often need to then be finetuned on hundreds of thousands of labeled CXRs to achieve high performance. Therefore, the current approach to model development is not viable on tasks with only a small amount of labeled data. An emerging method for reducing reliance on large amounts of labeled data is self-supervised learning (SSL), which uses unlabeled CXR datasets to automatically learn features that can be leveraged for downstream interpretation tasks. In this work, we investigated whether self-supervised pretraining methods could outperform traditional ImageNet pretraining for chest X-ray interpretation. We found that SSL-pretrained models outperformed ImageNet-pretrained models on thirteen different datasets representing high diversity in geographies, clinical settings, and prediction tasks. We thus show that SSL on unlabeled CXR data is a promising pretraining approach for a wide variety of CXR interpretation tasks, enabling a shift away from costly labeled datasets.

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“…Brax: Reis ( 2022 ) introduced the dataset which includes 40,967 CXRs, 24,959 imaging studies for 19,351 subjects, collected from the Hospital Israelita Albert Einstein, Brazil. The dataset is labeled for 14 radiological findings using report parsing (NLP).…”

Section: Datasetsmentioning

confidence: 99%

AI-based radiodiagnosis using chest X-rays: A review

2023

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Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

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BRAX, Brazilian labeled chest x-ray dataset

Cited by 16 publications

References 21 publications

German CheXpert Chest X-ray Radiology Report Labeler

German CheXpert Chest X-ray Radiology Report Labeler

Self-Supervised Pretraining Enables High-Performance Chest X-Ray Interpretation Across Clinical Distributions

AI-based radiodiagnosis using chest X-rays: A review

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