220 datasets found

The colorectal adenocarcinoma gland (CRAG) dataset is a collection of 213 H&E CRA images with different cancer grades.

The AMOS2022 challenge dataset contains 500 CT and 100 MRI scans, distributed into 200+40 (CT + MRI) training, 100+20 validation and 200+40 test images.

The dataset used for the federated learning experiment for semantic medical image segmentation.

Left atrial (LA) segmentation dataset for medical image segmentation

White matter hyperintensities (WMH) dataset for medical image segmentation

The dataset used in the paper is the 2D optic cup and disk segmentation dataset: Drishti-GS (GS) and the left atrial (LA) 3D gadolinium-enhanced magnetic resonance imaging (MRI)...

The BraTS 2017 dataset, used for training and testing the segmentation models.

The 2D brain tumour dataset proposed in [16], created from axial slices extracted from the MRI volumes of the 3D brain tumor segmentation challenge (BraTS) 2017 data.

Skin lesion segmentation (SLS) plays an important role in skin lesion analysis. Vision transformers (ViTs) are considered an auspicious solution for SLS, but they require more...

The dataset used for training and testing the proposed deep co-training method for semi-supervised image segmentation.

A public benchmark for the cardiac substructure segmentation task, which consists of 20 unpaired CT and 20 MRI images from 40 patients.

A medical cross-modality domain adaptation benchmark proposed in [6] to validate the performance of our proposed unsupervised cross-modality domain adaptation method for...

A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

The PET-CT dataset contains 3D images from 173 patients who were diagnosed with large B-cell lymphomas and underwent PET-CT examination.

Microscopy Images dataset was from the NeurIPS 2022 Cell Segmentation Challenge [34] that focused on cell segmentation in various microscopy images.

Endoscopy images dataset was from the MICCAI 2017 EndoVis Challenge [1] that focused on segmenting seven instruments from endoscopy images.

Abdomen MRI dataset was from the MICCAI 2022 AMOS Challenge [25] that also focused on abdominal organ segmentation.

Abdomen CT dataset was from the MICCAI 2022 FLARE Challenge [35] that focused on the segmentation of 13 abdominal organs, including the liver, spleen, pancreas, right kidney,...

The dataset contains images of medical images and corresponding labels.

The BTCV dataset contains abdominal CT images for segmentation tasks.