PadChest is a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score.
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IntrA is an open-access 3D intracranial aneurysm dataset that makes the application of points-based and mesh-based classification and segmentation models available. This dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction.
24 PAPERS • 2 BENCHMARKS
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels (0, 1+, 2+, 3+).
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There has been a rapidly growing interest in Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the machine learning research literature, aiming to assist doctors in telemedicine services. These systems are designed to interact with patients, collect evidence about their symptoms and relevant antecedents, and possibly make predictions about the underlying diseases. Doctors would review the interactions, including the evidence and the predictions, collect if necessary additional information from patients, before deciding on next steps. Despite recent progress in this area, an important piece of doctors' interactions with patients is missing in the design of these systems, namely the differential diagnosis. Its absence is largely due to the lack of datasets that include such information for models to train on. In this work, we present a large-scale synthetic dataset of roughly 1.3 million patients that includes a differential diagnosis, along with the ground truth
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The REFLACX dataset contains eye-tracking data for 3,032 readings of chest x-rays by five radiologists. The dictated reports were transcribed and have timestamps synchronized with the eye-tracking data.
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The LIMUC dataset is the largest publicly available labeled ulcerative colitis dataset that compromises 11276 images from 564 patients and 1043 colonoscopy procedures. Three experienced gastroenterologists were involved in the annotation process, and all images are labeled according to the Mayo endoscopic score (MES).
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This dataset is created from MIMIC-III (Medical Information Mart for Intensive Care III) and contains simulated patient admission notes. The clinical notes contain information about a patient at admission time to the ICU and are labelled for four outcome prediction tasks: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay.
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Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these t
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Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our medical imaging DICOM files of patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset of the used medical images during the UTA4 tasks. This repository and respective dataset should be paired with the dataset-uta4-rates repository dataset. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted on our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset repres
1 PAPER • 1 BENCHMARK
Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our severity rates (BIRADS) of clinicians while diagnosing several patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of severity rates (BIRADS) concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of bot
EBHI-Seg is a dataset containing 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer.
FractureAtlas is a musculoskeletal bone fracture dataset with annotations for deep learning tasks like classification, localization, and segmentation. The dataset contains a total of 4,083 X-Ray images with annotation in COCO, VGG, YOLO, and Pascal VOC format. This dataset is made freely available for any purpose. The data provided within this work are free to copy, share or redistribute in any medium or format. The data might be adapted, remixed, transformed, and built upon. The dataset is licensed under a CC-BY 4.0 license. It should be noted that to use the dataset correctly, one needs to have knowledge of medical and radiology fields to understand the results and make conclusions based on the dataset. It's also important to consider the possibility of labeling errors.
A dataset for medical consultation dialogues. See our related paper for more details: https://arxiv.org/pdf/2204.13953.pdf
Hypertention Disease Medication dataset.
Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process.