Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.
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The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/
A dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. Source: OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset consists of typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. This dataset also provides information on the disease severity of diabetic retinopathy and diabetic macular edema for each image. This dataset is perfect for the development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
The ACNE04 dataset includes 3756 Chinese face images with Acne. The ACNE04 dataset includes the annotations of local lesion numbers and global acne severity based on Hayashi Criterion.
Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of 1268 fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
Under a close collaboration with an expert radiologist team of the Hospital Universitario San Cecilio, the COVIDGR-1.0 dataset of patients' anonymized X-ray images has been built. 852 images have been collected following a strict labeling protocol. They are categorized into 426 positive cases and 426 negative cases. Positive images correspond to patients who have been tested positive for COVID-19 using RT-PCR within a time span of at most 24h between the X-ray image and the test. Every image has been taken using the same type of equipment and with the same format: only the posterior-anterior view is considered. Source: https://github.com/ari-dasci/covidgr
The NCT-CRC-HE-100K dataset is a set of 100,000 non-overlapping image patches extracted from 86 H$\&$E stained human cancer tissue slides and normal tissue from the NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). While the dataset Colorectal Cacner-Validation-Histology-7K (CRC-VAL-HE-7K) consist of 7180 images extracted from 50 patients with colorectal adenocarcinoma and were used to create a dataset that does not overlap with patients in the NCT-CRC-HE-100K dataset. It was created by pathologists by manually delineating tissue regions in whole slide images into the following nine tissue classes: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM). Image source: https://www.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf
Approx. 300,000 images of galaxies labelled by shape. Labels are from www.galaxyzoo.org volunteers, and are noisy. Images are from the DECaLS telescope survey. Also includes predictions from an ensemble of EfficientNets, each using MC Dropout and a novel probabilistic loss function.
The dataset contains 33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. Each image is associated with one of these individuals using a unique patient identifier. All malignant diagnoses have been confirmed via histopathology, and benign diagnoses have been confirmed using either expert agreement, longitudinal follow-up, or histopathology. A thorough publication describing all features of this dataset is available in the form of a pre-print that has not yet undergone peer review. The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital Clínic de Barcelona, Medical University of Vienna, Memorial Sloan Kettering Cancer Center, Melanoma Institute Australia, University of Queensland, and the University of Athens Medical School. The dataset was curated for the SIIM-ISIC Melanoma Classification Challenge hosted on Kaggle during the Summer of 2020. DOI: https://doi.org/10.34970/2020-ds01
NIH-CXR-LT. NIH ChestXRay14 contains over 100,000 chest X-rays labeled with 14 pathologies, plus a “No Findings” class. We construct a single-label, long-tailed version of the NIH ChestXRay14 dataset by introducing five new disease findings described above. The resulting NIH-CXR-LT dataset has 20 classes, including 7 head classes, 10 medium classes, and 3 tail classes. NIH-CXR-LT contains 88,637 images labeled with one of 19 thorax diseases, with 68,058 training and 20,279 test images. The validation and balanced test sets contain 15 and 30 images per class, respectively.
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).
MIMIC-CXR-LT. We construct a single-label, long-tailed version of MIMIC-CXR in a similar manner. MIMIC-CXR is a multi-label classification dataset with over 200,000 chest X-rays labeled with 13 pathologies and a “No Findings” class. The resulting MIMIC-CXR-LT dataset contains 19 classes, of which 10 are head classes, 6 are medium classes, and 3 are tail classes. MIMIC-CXR-LT contains 111,792 images labeled with one of 18 diseases, with 87,493 training images and 23,550 test set images. The validation and balanced test sets contain 15 and 30 images per class, respectively.
CheXphoto is a competition for x-ray interpretation based on a new dataset of naturally and synthetically perturbed chest x-rays hosted by Stanford and VinBrain. Chest radiography is the most common imaging examination globally, and is critical for screening, diagnosis, and management of many life threatening diseases. Most chest x-ray algorithms have been developed and validated on digital x-rays, while the vast majority of developing regions use films. An appealing solution to scaled deployment is to leverage the ubiquity of smartphones for automated interpretation of film through cellphone photography. Automated interpretation of photos of chest x-rays at the same high-level of performance as with digital chest x-rays is challenging because photographs of x-rays introduce visual artifacts not commonly found in digital x-rays. To encourage high model performance for this application, we developed CheXphoto, a dataset of photos of chest x-rays and synthetic transformations designed to mimic the effects of photography. With the launch of the CheXphoto competition, we are pleased to announce the release of an additional set of x-ray film images provided by VinBrain, a subsidiary of Vingroup. Please see Validation and Test Sets for details.
LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images. It encompasses approximately 80,000 high-resolution 64x64 images, meticulously categorized into three primary classes: B cells, T4 cells, and T8 cells. Dataset Characteristics: Size: ~80,000 images Resolution: 64x64 pixels Classes: B cells, T4 cells, T8 cells Format: MNIST-like standardized biomedical imagery Modality: Microscopy-based high-resolution cell images Motivation and Summary: LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is vast in scale and rich in detail. It supports a wide array of research endeavors, from fundamental biological studies to advanced computational model development. Potential Use Cases: Medical Research: Studying lymphocyte morphology and characteristics Machine Learning & AI: Developing and evaluating image classification models AutoML & Benchmarking: Serving as a benchmark dataset for automated model training and performance evaluation Educational Purposes: Teaching deep learning concepts in biomedical imaging Data Collection Process: The dataset comprises high-resolution images of lymphocytes obtained through microscopy. Each image is standardized to a 64x64 pixel resolution to maintain consistency and facilitate analysis. Annotations and Labels: Each image is labeled as one of the three lymphocyte classes: B cells, T4 cells, or T8 cells. The labeling process was conducted by experts in the field to ensure accuracy.
A multi-tasking oral ulcer dataset (Autooral dataset) is proposed. Autooral dataset contains two major tasks of disease segmentation and classification. The labeling of the Autooral dataset was done by three experienced dentists. At the end of the annotation, we formed 420 images of oral data with high quality after cropping and removal operations. We standardize the image size to 256*256. The original image is a 24-bit RGB image, the ground truth for the segmentation task is an 8-bit image, and there are five different disease types for the classification task (including cancerous ulcers, traumatic ulcers and traumatic blood blisters, herpes-like aphthous ulcers, mild aphthous ulcers, severe aphthous ulcers). The ratio of the 5 different ulcer types for the classification task was 9:9:15:18:22 (with a few exclusions). Further, by chi-square test, there were significant differences in gender (p=0.04) and age (p=0.01) of the patients among the 5 ulcer types. All-age coverage, a 13-year collection interval, and the presence of 12 underlying diseases, among other things, demonstrated that we had sufficient sample diversity.
ENSeg Dataset Overview This dataset represents an enhanced subset of the ENS dataset. The ENS dataset comprises image samples extracted from the enteric nervous system (ENS) of male adult Wistar rats (Rattus norvegicus, albius variety), specifically from the jejunum, the second segment of the small intestine. The original dataset consists of two classes: - Control (C): Healthy animals. - Walker-256 Tumor (WT): Animals with cancer induced by the Walker-256 Tumor. Image acquisition involved 13 different animals, with 7 belonging to the C class and 6 to the WT class. Each animal contributed 32 image samples obtained from the myenteric plexus. All images were captured using the same setup and configuration, stored in PNG format, with a spatial resolution of 1384 × 1036 pixels. The overall process of obtaining the images and performing the morphometric and quantitative analyses takes approximately 5 months. ### Dataset Annotations Our dataset version includes expert-annotated labels for 6 animals, tagged as 2C, 4C, 5C, 22WT, 23WT, and 28WT. The image labels were created by members of the same laboratory where the images originated: researchers from the Enteric Neural Plasticity Laboratory of the State University of Maringá (UEM). Annotations were generated using LabelMe and consist of polygons marking each neuron cell. To maintain labeling quality according to laboratory standards, only neuron cells with well-defined borders were included in the final label masks. The labeling process lasted 9 months (from November 2023 to July 2024) and was iteratively reviewed by the lead researcher of the lab. ### Dataset Statistics After processing, the full dataset contains: - 187 images - 9,709 annotated neuron cells The table below summarizes the number of images and annotated neurons per animal tag. | Animal Tag | # of Images | # of Neurons | |------------|------------|--------------| | 2C | 32 | 1590 | | 4C | 31 | 1513 | | 5C | 31 | 2211 | | 22WT | 31 | 1386 | | 23WT | 31 | 1520 | | 28WT | 31 | 1489 | | Total | 187 | 9709 | ### Recommended Training Methodology Due to the limited number of animal samples and the natural split of images, we recommend using a leave-one-out cross-validation (LOO-CV) method for training. This ensures more reliable results by performing six training sessions per experiment, where: - In each session, images from one subject are isolated for testing. - The remaining images are used for training. Although cross-validation is recommended, it is not mandatory. Future works may introduce new training methodologies as the number of annotated subjects increases. However, it is crucial to maintain images from the same source (animal) within the same data split to prevent biased results. Even though samples are randomly selected from the animals' original tissues, this approach enhances the credibility of the findings. This dataset provides a valuable resource for instance segmentation and biomedical image analysis, supporting research on ENS morphology and cancer effects. Contributions and feedback are welcome! ## Project Citation If you want to cite our article, the dataset, or the source codes contained in this repository, please used the citation (bibtex format): @Article{felipe25enseg, AUTHOR = { Felipe, Gustavo Zanoni and Nanni, Loris and Garcia, Isadora Goulart and Zanoni, Jacqueline Nelisis and Costa, Yandre Maldonado e Gomes da}, TITLE = {ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images}, JOURNAL = {Applied Sciences}, VOLUME = {15}, YEAR = {2025}, NUMBER = {3}, ARTICLE-NUMBER = {1046}, URL = {https://www.mdpi.com/2076-3417/15/3/1046}, ISSN = {2076-3417}, DOI = {10.3390/app15031046} } ## Additional Notes Please check out our previous datasets if you are interest into developing projects with Enteric Nervous System images: 1. EGC-Z: three datasets of Enteric Glial cells images, composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. Each dataset represent binary classification task, with the classes: control (healthy) and sick; 2. ENS: the ENS image datasets comprises 1248 images taken from thirteen rats distributed in two classes: control/healthy or sick. The images were created with three distinct contrast settings targeting different Enteric Nervous System cells: Enteric Neuron cells, Enteric Glial cells, or both. For more details, please contact the main author of this project or create an issue on the project.
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.
Heel Bone X-Ray Dataset consists of 3,956 X-ray images of the foot, primarily focused on detecting and classifying heel bone diseases. The images were obtained from Kirkuk General Hospital in Digital Imaging and Communications in Medicine (DICOM) format and converted to JPG format using the MicroDicom tool. The dataset is labeled into three distinct classes: Normal: 1,842 images Heel Spur: 1,316 images Severe (Heel Spur Complications): 798 images All labels were assigned by a specialist in orthopedics and traumatology and were cross-verified by a radiology specialist for validation, ensuring high-quality annotations. This dataset is valuable for training models on medical image classification, specifically targeting foot-related conditions. Dataset Size: 3,956 images Image Format: JPG (originally in DICOM format) Source: Kirkuk General Hospital https://www.kaggle.com/datasets/osamahtaher/heel-dataset/data Paper: HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray images. Taher, Osamah, and Kasım Özacar. "HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X‐Ray images." International Journal of Imaging Systems and Technology 34.3 (2024): e23084.
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