Medical Diagnosis

ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia. Source: https://nihcc.app.box.com/v/ChestXray-NIHCC/file/220660789610 Image Source: https://nihcc.app.box.com/v/ChestXray-NIHCC

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.

Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7] Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative

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. 103 3D models of entire brain vessels are collected by reconstructing scanned 2D MRA images of patients (the raw 2D MRA images are not published due to medical ethics). 1909 blood vessel segments are generated automatically from the complete models, including 1694 healthy vessel segments and 215 aneurysm segments for diagnosis. 116 aneurysm segments are divided and annotated manually by medical experts; the scale of each aneurysm segment is based on the need for a preoperative examination. Geodesic distance matrices are computed and included for each annotated 3D segment, because the expression of the geodesic distance is more accurate than Euclidean distance according to the shape of vessels. Source: https://github.com/intra3d2019/IntrA Image Source: https://github.com/intra3d2019/IntrA

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+).

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 pathology, symptoms and antecedents for each patient. Unlike existing datasets which only contain binary symptoms and antecedents, this dataset also contains categorical and multi-choice symptoms and antecedents useful for efficient data collection. Moreover, some symptoms are organized in a hierarchy, making it possible to design systems able to interact with patients in a logical way. As a proof-of-concept, we extend two existing AD and ASD systems to incorporate the differential diagnosis, and provide empirical evidence that using differentials as training signals is essential for the efficiency of such systems or for helping doctors better understand the reasoning of those systems.

An open-source Optical Coherence Tomography Image Database containing different retinal OCT images with various pathological conditions. This comprehensive open-access database contains over 500 high-resolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR).

MedConceptsQA - Open Source Medical Concepts QA Benchmark The benchmark can be found here: https://huggingface.co/datasets/ofir408/MedConceptsQA

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. Localization labels for abnormalities are very costly, and the collection of eye-tracking data and reports for implicit localization labels may be an alternative for scaling up data collection. One of the potential uses for these data is in additional supervision for training computer vision models. For more details, check the Physionet page and the dataset description paper ("REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays").

The complete blood count (CBC) dataset contains 360 blood smear images along with their annotation files splitting into Training, Testing, and Validation sets. The training folder contains 300 images with annotations. The testing and validation folder both contain 60 images with annotations. We have done some modifications over the original dataset to prepare this CBC dataset where some of the image annotation files contain very low red blood cells (RBCs) than actual and one annotation file does not include any RBC at all although the cell smear image contains RBCs. So, we clear up all the fallacious files and split the dataset into three parts. Among the 360 smear images, 300 blood cell images with annotations are used as the training set first, and then the rest of the 60 images with annotations are used as the testing set. Due to the shortage of data, a subset of the training set is used to prepare the validation set which contains 60 images with annotations.

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).

The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations. This dataset is designed to facilitate advanced machine learning tasks in gastrointestinal (GI) diagnostics, including image captioning, Visual Question Answering (VQA) and text-based generation of synthetic medical images.

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 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 both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

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. To obtain the data one first has to gain access to the MIMIC-III dataset and then run the scripts introduced in the linked repository.

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification. Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist. Results: The dataset contains 1613 CT volumes with 2487 annotated nodules. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%. Conclusions: The Duke Lung Nodule Dataset is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.

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. Furthermore, any publication that utilizes this resource should acknowledge the original paper, and the authors are encouraged to share their code and models to assist the research community in replicating the experiments and promoting the field of medical imaging.

The goal of the challenge is to compare automated algorithms that are able to detect and segment various types of fluids on a common dataset of optical coherence tomography (OCT) volumes representing different retinal diseases, acquired with devices from different manufacturers. We made available a dataset of OCT volumes containing a wide variety of retinal fluid lesions with accompanying reference annotations. We invite the medical imaging community to participate by developing and testing existing and novel automated retinal OCT segmentation methods.

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 represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

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 both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

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. Source: EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks Image Source: https://arxiv.org/pdf/2212.00532v1.pdf

A dataset for medical consultation dialogues. See our related paper for more details: https://arxiv.org/pdf/2204.13953.pdf

Hypertention Disease Medication dataset.

The Liver-US dataset is a comprehensive collection of high-quality ultrasound images of the liver, including both normal and abnormal cases. This dataset is designed to facilitate research in medical image classification, with a focus on liver-related conditions. It includes a diverse range of ultrasound images acquired from multiple clinical settings, providing a robust foundation for developing and validating machine learning models in medical image analysis. Detailed Dataset Description Dataset Characteristics: The Liver-US dataset contains735 ultrasound images of the liver, including both normal and abnormal cases. The dataset includes a variety of liver conditions, such as Hepatocellular Carcinoma (HCC), liver cysts, hemangiomas, and other focal liver lesions. The images were acquired using state-of-the-art ultrasound equipment, ensuring high resolution and contrast for accurate diagnosis. Motivations and Content Summary: The primary motivation behind the creation of the Liver-US dataset is to provide a comprehensive and diverse resource for researchers working on medical image classification task related to the liver. The dataset was compiled in collaboration with leading medical institutions and includes images from a diverse patient population, ensuring representativeness and generalizability. The annotations were performed by experienced radiologists, adhering to strict quality control standards to ensure accuracy and reliability. Potential Use Cases: Research and Development: The dataset is ideal for researchers and developers working on machine learning models for medical image classification and feature extraction. Clinical Applications: It can be used to train and validate computer-aided diagnosis systems for early detection and diagnosis of liver conditions. Educational Purposes: The dataset serves as a valuable resource for training medical professionals and students in the interpretation of ultrasound images and the diagnosis of liver conditions. Benchmarking: It provides a standardized benchmark for comparing the performance of different algorithms and models in the field of medical image analysis.

LLM Health Benchmarks Dataset The Health Benchmarks Dataset is a specialized resource for evaluating large language models (LLMs) in different medical specialties. It provides structured question-answer pairs designed to test the performance of AI models in understanding and generating domain-specific knowledge. ### Primary Purpose This dataset is built to: - Benchmark LLMs in medical specialties and subfields. - Assess the accuracy and contextual understanding of AI in healthcare. - Serve as a standardized evaluation suite for AI systems designed for medical applications. #### Key Features - Covers 50+ medical and health-related topics, including both clinical and non-clinical domains. - Includes ~7,500 structured question-answer pairs. - Designed for fine-grained performance evaluation in medical specialties. #### Applications - LLM Evaluation: Benchmarking AI models for domain-specific performance. - Healthcare AI Research: Standardized testing for AI in healthcare. - Medical Education AI: Testing AI systems designed for tutoring medical students. ### Dataset Structure The dataset is organized by medical specialties and subfields, each represented as a split. Below is a snapshot: | Specialty | Number of Rows | |-----------------------------|--------------------| | Lab Medicine | 158 | | Ethics | 174 | | Dermatology | 170 | | Gastroenterology | 163 | | Internal Medicine | 178 | | Oncology | 180 | | Orthopedics | 177 | | General Surgery | 178 | | Pediatrics | 180 | | ...(and more) | ... | Each split contains: - Questions: The medical questions for the specialty. - Answers: Corresponding high-quality answers. ### Usage Instructions Here’s how you can load and use the dataset: python from datasets import load_dataset ## Load the dataset dataset = load_dataset("yesilhealth/Health_Benchmarks") ## Access specific specialty splits oncology = dataset["Oncology"] internal_medicine = dataset["Internal_Medicine"] ## View sample data print(oncology[:5]) ### Evaluation Workflow 1. Model Input: Provide the questions from each split to the LLM. 2. Model Output: Collect the AI-generated answers. 3. Scoring: Compare model answers to ground truth answers using metrics such as: - Exact Match (EM) - F1 Score - Semantic Similarity ### Citation If you use this dataset for research or development, please cite: plaintext @dataset{yesilhealth_health_benchmarks, title={Health Benchmarks Dataset}, author={Yesil Health AI}, year={2024}, url={https://huggingface.co/datasets/yesilhealth/Health_Benchmarks} } ### License This dataset is licensed under the Apache 2.0 License. ### Feedback For questions, suggestions, or feedback, feel free to contact us via email at [hello@yesilhealth.com].

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. The motivation of Niramai Oncho Dataset is to develop algorithms that can detect Onchocerca worms non-invasively using thermal imaging. The dataset consists of both thermal images and videos captured for each nodule site during imaging of a participant. Histopathological confirmation of the presence of female adult worm is used to obtain the ground truth. In total, the dataset consists of 125 participants' data with 192 palpable nodules. Out of 192 palpable nodules, 101 correspond to live female nodules and the remain 91 correspond to dead nodules.

Sakha-TB is a de-identified image dataset of frontal chest X-rays (CXR), collected through collaboration with several medical institutions in the Republic of Sakha (Yakutia, Russia). The set contains 400 normal X-rays and 400 X-rays with manifestations of pulmonary tuberculosis, balanced to some extent by age and gender, in 16-bit and 8-bit lossless PNG format, converted directly from DICOM files without any changes. Pchelintsev, Y., Khvostikov, A., Buchatskaia, O. et al. Robustness Analysis of Chest X-Ray Computer Tuberculosis Diagnosis. Comput Math Model 33, 472–486 (2022). https://doi.org/10.1007/s10598-023-09587-0