26 datasets found

Three datasets for phase recognition, pupil segmentation, and lens segmentation that are publicly released to support reproducibility and allow further investigations for lens...

The MedMNIST dataset is a large standardized biomedical image collection, including 12 number of 2D datasets and 6 number of 3D datasets.

Semantic object segmentation is a fundamental task in medical image analysis and has been widely used in automatic delineation of regions of interest in 3D medical images, such...

A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently....

The DRIVE dataset contains the curvilinear-shaped vessel. This dataset consists of 40 565 × 584 color retinal images, which are split into 20 training images and 20 test images.

HISTO-FED is a medical image dataset, consisting of both public and private hematoxylin & eosin (H&E) stained histological whole-slide images of human colorectal cancer...

The MICCAI 2009 LV Segmentation Challenge (LV-09) dataset contains 45 subjects with expert annotations.

The UMN dataset consists of three different crowd scenes, and the dataset has 11 videos from these scenes, with a resolution of 240 × 320. Each video sequence represents a...

The KERMANY dataset contains 256 OCT scans from DME patients.

The OPTIMA dataset contains 30 OCT volumes divided into training and testing sets, each set containing 15 volumes.

Three public datasets namely OPTIMA, KERMANY, and UMN were employed to evaluate the proposed method.

Medical images, captions, and textual references

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms,...

One-pass multi-task convolutional neural networks for efficient brain tumor segmentation.

No NewNet: A simple and efficient approach to brain tumor segmentation.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

A real CT image database about pancreatitis from hospitals, built to evaluate the performance of the proposed method for pancreatitis recognition.

The V-Net is a deep learning model for medical image segmentation that uses a U-Net architecture.

The dataset used in this paper for medical image segmentation, including images from the ISBI-2012 dataset.