This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center.

The dataset includes:

1. SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here.
2. MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here.
3. Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).
4. Additional anonymized data: TBA

These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.

Medical Imaging (CT-Xray) Colorization New Dataset 🩺💻🖼️ This dataset provides a collection of medical imaging data, including both CT (Computed Tomography) and X-ray images, with an added focus on colorization techniques. The goal of this dataset is to facilitate the enhancement of diagnostic processes by applying various colorization techniques to grayscale medical images, allowing researchers and machine learning models to explore the effects of color in radiology.

Key Features: CT and X-ray Images 🏥: Contains both CT scans and X-ray images, widely used in medical diagnostics. Colorized Medical Images 🌈: Each image has been colorized using advanced methods to improve visual interpretation and analysis, including details that might not be immediately obvious in grayscale images. New Dataset 📊: This dataset is newly created to provide high-quality colorized medical imaging, ideal for training AI models in medical image analysis and enhancing diagnostic accuracy. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F4bfb7257cf09b0a118808b289c6c3ed4%2Fmotion_image.gif?generation=1742292037458801&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F20c64287d3b580a36bf8f948f82dbb6b%2Fmotion_image2.gif?generation=1742292060396551&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2Fdb91cac64f5a6a9100ac117fc8a55ee5%2Fmotion_image4.gif?generation=1742292150147491&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F8624a8cab05645e3a5f02a2c1e3e9e3f%2Fmotion_image3.gif?generation=1742292165846162&alt=media" alt="">

Methods Used for Colorization: Basic Color Map Application 🎨: Applying standard color maps to highlight structures in CT and X-ray images. Adaptive Histogram Equalization (CLAHE) 🔍: Adaptive enhancement to improve contrast and highlight important features, especially in medical contexts. Contrast Stretching 📈: Adjusting image intensity to enhance visual details and improve diagnostic quality. Gaussian Blur 🌀: Applied to reduce noise, offering a smoother image for better processing. Edge Detection (Canny) ✨: Detecting edges and contours, useful for identifying specific features in medical scans. Random Color Palettes 🎨: Using randomized color schemes for unique visual representations. Gamma Correction 🌟: Adjusting image brightness to reveal more information hidden in the shadows. LUT (Lookup Table) Color Mapping 💡: Applying predefined color lookups for visually appealing representations. Alpha Blending 🔶: Blending colorized regions based on certain thresholds to highlight structures or anomalies. 3D Rendering 🔺: For creating 3D-like visualizations from 2D scans. Heatmap Visualization 🔥: Highlighting areas of interest, such as anomalies or tumors, using heatmap color gradients. Interactive Segmentation 🖱️: Interactive visualizations that help in segmenting regions of interest in medical images. Applications 🏥💡 This dataset has numerous applications, particularly in the field of medical image analysis, AI development, and diagnostic improvement. Some of the major applications include:

Medical Diagnostics Enhancement 🔍:

Colorization can aid radiologists in interpreting CT and X-ray images by making abnormalities more visible. Helps in visualizing tumors, fractures, or other anomalies, especially in cases where grayscale images are hard to interpret. AI and Machine Learning for Healthcare 🤖:

Used for training deep learning models in image segmentation, detection, and classification of diseases (e.g., cancer detection). AI models can be trained on these colorized images to improve accuracy in diagnostic tools, leading to early disease detection. Medical Image Enhancement 🖼️:

Enables improved contrast, better detail visibility, and highlighting of specific anatomical regions using color. Colorization may improve the accuracy of radiological assessments by allowing professionals to more easily spot abnormalities and changes over time. Data Augmentation for Model Training 📚:

The colorized images can serve as an additional data source for training AI models, increasing model robustness through synthetic data generation. Various colorization methods (like heatmaps and random palettes) can be used to augment image variations, improving model performance under different conditions. Visualizing Anomalies for Anomaly Detection 🔥:

Heatmap visualization helps detect subtle and hidden anomalies by coloring the areas of interest with intensity, enabling faster identification of potential issues. Edge detection and segmentation techniques enhance the ability to detect the edges and boundaries of tumors, fractures, and other critical features. 3D Image Rendering for Detailed Analysis 🧠:

3D rend...

Dataset

This dataset was created by v_parth7

Released under Apache 2.0

Contents

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was created through a collaboration between the RSNA and Society of Thoracic Radiology (STR). Clinical annotation by thoracic radiology subspecialists was performed for all COVID positive chest radiography (CXR) imaging studies using a labeling schema based upon guidelines for reporting classification of COVID-19 findings in CXRs (see Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language, Journal of Thoracic Imaging).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) consists of 998 chest x-rays from 361 patients at four international sites annotated with diagnostic labels.

Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.

Data Abstract

1. 998 Chest x-ray examinations from 361 patients.

2. Annotations with labels:

   1. Classification

      • Typical Appearance
        Multifocal bilateral, peripheral opacities, and/or Opacities with rounded morphology
        Lower lung-predominant distribution (Required Feature - must be present with either or both of the first two opacity patterns)

      • Indeterminate Appearance
        Absence of typical findings AND Unilateral, central or upper lung predominant distribution of airspace disease

      • Atypical Appearance
        Pneumothorax or pleural effusion, Pulmonary Edema, Lobar Consolidation, Solitary lung nodule or mass, Diffuse tiny nodules, Cavity

      • Negative for Pneumonia
        No lung opacities

   2. Airspace Disease Grading
      Lungs are divided on frontal chest xray into 3 zones per lung (6 zones total). The upper zone extends from the apices to the superior hilum. The mid zone spans between the superior and inferior hilar margins. The lower zone extends from the inferior hilar margins to the costophrenic sulci.

      • Mild - Required if not negative for pneumonia
        Opacities in 1-2 lung zones

      • Moderate - Required if not negative for pneumonia
        Opacities in 3-4 lung zones

      • Severe - Required if not negative for pneumonia
        Opacities in >4 lung zones

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

Combined Medical Imaging Dataset

  Dataset Description

Combined medical imaging dataset with 6793 samples in Alpaca instruction format.

  Dataset Statistics

Total Samples: 6793 Training Samples: 5434 Validation Samples: 1359

  Modality Distribution

X-ray: 2691 samples CT: 2257 samples Unknown: 1329 samples MRI: 369 samples Ultrasound: 147 samples

  Source Distribution

ROCO: 5000 samples VQA-RAD: 1793 samples

  Sources

ROCO… See the full description on the dataset page: https://huggingface.co/datasets/robailleo/medical-imaging-combined.

This dataset is based on the widely used Kvasir Dataset, specifically focusing on two classes: normal-cecum and polyps. To enhance the dataset's utility for medical imaging classification, several steps were taken:

Data Augmentation: Basic augmentations such as flipping, rotating, and mirror imaging were applied. This increases the diversity of the dataset, helping models generalize better during training.

Incremental Learning: The augmented dataset is divided into three training sets designed for incremental learning. This setup allows researchers to explore and implement incremental learning algorithms effectively.

CLAHE Preprocessing: All images were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). CLAHE is highly effective in medical imaging as it enhances the contrast of the images, making it easier for models to detect and classify abnormalities.

Purpose and Applications The primary motivation behind creating this dataset is to facilitate advancements in medical imaging classification. By providing a dataset that is preprocessed and augmented, it aims to save researchers time and effort, allowing them to focus on developing and testing their models.

While the best use case for this dataset is medical imaging classification, its potential applications are vast. Researchers can use this dataset for various computer vision tasks, including segmentation, anomaly detection, and more.

Unique Features Augmented Dataset: Unlike the original Kvasir Dataset, this dataset is fully augmented for better training and testing. Incremental Learning Sets: The dataset is divided into three training sets to support incremental learning research. CLAHE Preprocessing: All images are preprocessed using CLAHE, which is particularly effective in enhancing medical images. Inspiration This dataset is inspired by the need for high-quality, preprocessed medical imaging datasets in the field of computer vision. It has been used in my own research to develop and test models for medical image classification. By sharing this dataset, I hope to contribute to the broader research community and help others create new projects and insights in medical imaging.

Medical Imaging

## Overview

Medical Imaging is a dataset for object detection tasks - it contains Thyroid Nodules annotations for 1,079 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Access comprehensive MRI images of spinal Meningioma cancer. Perfect for advancing medical research, diagnosis, and AI model training in oncology.

On 11 May 2016, the Court of Auditors published a report on medical imaging, requested by the Senate Social Affairs Committee, pursuant to Article LO 132-3-1 of the Financial Courts Code. Medical imaging, which has been actively involved in medical progress since its invention at the end of the 19th century and whose techniques have diversified (radiography, scan, ultrasound, MRI, scintigraphy), today faces strong medico-economic challenges that regulatory policies do not allow to treat satisfactorily. In order to better adapt the means to the needs, the Court recommends acting concurrently on three levers: improve the relevance of actions and encourage innovation by reallocating resources, reorganising the supply around mutualisations between healthcare institutions and between the hospital and the liberal sector, and, finally, upgrading hospital imaging by introducing more flexibility in the exercise of functions and practices. The Court makes eight recommendations.

This report is available on the Court’s website.

The published files correspond to the data used in the preparation of the report.

The respective data is comprised of 5 different datasets of medical images collected by the contributors, which can be used for classifying Lung Cancer, Bone Fracture, Brain tumor, Skin Lesions, and Renal Malignancy, respectively. The data also includes multiple disease and malignancy images for the respective dataset. The classification for the diseases can be done by using ResNet50 CNN architecture and other DCNN models. This data is also been used in a research article by the contributor.

Medical Image DataSet: Brain Tumor Detection

Medical Image Dataset: Brain Tumor Detection

The Brain Tumor MRI dataset, curated by Roboflow Universe, is a comprehensive dataset designed for the detection and classification of brain tumors using advanced computer vision techniques. It comprises 3,903 MRI images categorized into four distinct classes:

• Glioma: A tumor originating from glial cells in the brain.
• Meningioma: Tumors arising from the meninges, the protective layers surrounding the brain and spinal cord.
• Pituitary Tumor: Tumors located in the pituitary gland, affecting hormonal balance.
• No Tumor: MRI scans that do not exhibit any tumor presence.

Each image in the dataset is annotated with bounding boxes to indicate tumor locations, facilitating object detection tasks precisely. The dataset is structured into training (70%), validation (20%), and test (10%) sets, ensuring a robust framework for model development and evaluation.

The primary goal of this dataset is to aid in the early detection and diagnosis of brain tumors, contributing to improved treatment planning and patient outcomes. By offering a diverse range of annotated MRI images, this dataset enables researchers and practitioners to develop and fine-tune computer vision models with high accuracy in identifying and localizing brain tumors.

This dataset supports multiple annotation formats, including YOLOv8, YOLOv9, and YOLOv11, making it versatile and compatible with various machine-learning frameworks. Its integration with these formats ensures real-time and efficient object detection, ideal for applications requiring timely and precise results.

By leveraging this dataset, researchers and healthcare professionals can make significant strides in developing cutting-edge AI solutions for medical imaging, ultimately supporting more effective and accurate diagnoses in clinical settings.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2Fe03fba81bb62e32c0b73d6535a25cb8d%2F3.jpg?generation=1734173601629363&alt=media" alt="">

The Medical Imaging Management Market Report is Segmented by System (Picture Archiving and Communications System (PACS), Vendor-Neutral Archive (VNA), Application-Independent Clinical Archive (AICA), and More), Deployment Mode (On-Premise, Cloud-Based and Hybrid), End User (Hospitals, Diagnostic Imaging Centers, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

A comprehensive dataset of de-identified brain MRI scans used for tumor detection, including T1-weighted, T2-weighted, and contrast-enhanced images with annotation-ready data ideal for training AI and machine learning systems in medical imaging.

Lumbar Spine MRI dataset - 2,400,000+ Studies

Dataset comprises 2,400,000+ medical studies featuring MRI images of the spine alongside corresponding text reports from radiologists, including detailed descriptions, conclusions, and recommendations. It covers 67+ pathologies and is designed for medical imaging analysis, spine segmentation, and diagnostic research. - Get the data

  Dataset characteristics:

Characteristic Data

Description MRI images of the lumbar… See the full description on the dataset page: https://huggingface.co/datasets/ud-medical/Spine-MRI-Dataset.

X-ray Reports Dataset

This dataset contains high-quality (“A-grade”) anonymized X-ray images paired with radiology reports. It has been carefully curated, cleaned, and verified to ensure accuracy, completeness, and compliance with privacy standards (e.g., HIPAA/GDPR), making it suitable for high-stakes or research-grade model training.

  Contact

For queries or collaborations related to this dataset, contact:

anoushka@kgen.io
abhishek.vadapalli@kgen.io

  Supported… See the full description on the dataset page: https://huggingface.co/datasets/KAI-KratosAI/medical-imaging.

Automated radiology reporting represents a pivotal advancement in medical imaging diagnos- tics, offering a solution to the time-consuming process of manual report generation. In this study, we propose an innovative methodology leveraging OpenAI’s Generative Pre-trained Transformer (GPT) models for the automated generation of descriptive radiology reports in multimodal medical imaging. Our approach integrates state-of-the-art natural language processing techniques with im- age processing methodologies to synthesize comprehensive reports from diverse image modalities, including computed tomography (CT) scans and ultrasounds. Through rigorous experimenta- tion on a curated dataset of radiology images, encompassing various pathological conditions and anatomical structures, we demonstrate the efficacy of our methodology in producing clinically relevant and coherent reports. Our findings underscore the potential of GPT-powered systems in augmenting radiologists’ workflows and improving diagnostic efficiency in medical imaging in- terpretation. Additionally, we emphasize that the dataset used in this research is obtained from Kaggle, a renowned platform for sharing and accessing diverse datasets, ensuring the accessibility and reproducibility of our experiments.

The graph shows the number of articles published in the discipline of ^.

Global medical imaging market size is expected to grow from USD 43.93 bn in 2024 to USD 46.41 bn in 2025 and USD 72.39 bn by 2035, representing a CAGR of 4.5%.

The Medical Imaging Software Market report segments the industry into By Imaging Type (2D Imaging, 3D Imaging, 4D Imaging), By Application (Dental Applications, Orthopaedic Applications, Cardiology Applications, Obstetrics and Gynaecology Applications, Mammography Applications, Urology and Nephrology Applications, Other Applications), and Geography (North America, Europe, Asia-Pacific, Rest of the World).

The Diagnostic Imaging Dataset (DID) is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients, extracted from local Radiology Information Systems (RISs) and submitted monthly. The DID captures information about referral source and patient type, details of the test (type of test and body site), demographic information such as GP registered practice, patient postcode, ethnicity, gender and date of birth, plus items about waiting times for each diagnostic imaging event, from time of test request through to time of reporting. This data is published on the NHS England website. Please follow the link below.