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13 results for "mimic-cxr labels"
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Application of Med-PaLM 2 in the refinement of MIMIC-CXR labels
MIMIC-CXR is a large, open source dataset that is widely-used in medical AI research. One of the limitations of this dataset is the lack of ground truth labels for the chest X-ray studies. Prior work has extracted structured labels from the MIMIC-CX…
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MIMIC-CXR-JPG - chest radiographs with structured labels
The MIMIC Chest X-ray JPG (MIMIC-CXR-JPG) Database v2.0.0 is a large publicly available dataset of chest radiographs in JPG format with structured labels derived from free-text radiology reports. The MIMIC-CXR-JPG dataset is wholly derived from MIMI…
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Visual Question Answering evaluation dataset for MIMIC CXR
MIMIC CXR [1] is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. In addition, labels for the presence of 12 different chest-related pathologies, as well as of any support devices, and overall…
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Pulmonary Edema Severity Grades Based on MIMIC-CXR
Clinical management decisions for patients with acutely decompensated heart failure and many other diseases are often based on grades of pulmonary edema severity, rather than its mere absence or presence. Chest radiographs are commonly performed to …
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MIMIC-CXR-Ext-ILS: Lesion Segmentation Masks and Instruction-Answer Pairs for Chest X-rays
The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on complex, expert-level text inputs, creating a barrier to practical use. To address these lim…
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CAD-Chest: Comprehensive Annotation of Diseases based on MIMIC-CXR Radiology Report
Several extant chest X-ray (CXR) datasets predominantly comprise binary disease labels and exhibit a deficiency in providing comprehensive disease-related information. Crucial facets of disease management, including disease severity, diagnostic unce…
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RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
RadGraph is a dataset of entities and relations in full-text radiology reports. We designed a novel information extraction (IE) schema to structure clinical information in a radiology report with four entities and three relations. Our train set cons…
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RadNLI: A natural language inference dataset for the radiology domain
The problem of natural language inference (NLI) determines whether a natural language hypothesis can be justifiably inferred from a natural language premise. NLI has attracted researchers to benchmark it in a number of settings including medical one…
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Medical-CXR-VQA dataset: A Large-Scale LLM-Enhanced Medical Dataset for Visual Question Answering on Chest X-Ray Images
Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficien…
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Chest ImaGenome Dataset
In recent years, with the release of multiple large datasets, automatic interpretation of chest X-ray (CXR) images with deep learning models have become feasible for specific abnormalities or for generating preliminary reports. However, despite repo…