MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification
TL
TL;DR
Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label).
Abstract
Measurement(s) supervised machine learning Technology Type(s) machine learning We introduce MedMNIST v2 , a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28 × 28 (2D) or 28 × 28 × 28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 9,998 3D images in total, could support numerous research/educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D/3D neural networks and open-source/commercial AutoML tools. The data and code are publicly available at https://medmnist.com/ .
References41 items
1
DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge
2
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
3
The Medical Segmentation Decathlon
4
Elastic Net Nonparallel Hyperplane Support Vector Machine and Its Geometrical Rationality
5