53 datasets found

Dataset for abdominal organ segmentation

The REFUGE-2 dataset is an active challenge on classification of clinical Glaucoma.

The proposed framework is composed of two networks (see Fig 1). The encoder network q with parameters of φ computes qφ(z|x) : xi → zi. The encoder maps an input image xi ∈ X to...

BloodMNIST is a medical imaging dataset containing 9,000 images of blood samples, each with a size of 224x224x3.

RetinaMNIST is a medical imaging dataset containing 5,000 images of retinal scans, each with a size of 224x224x3.

The OrganAMNIST dataset is a collection of abdominal CT images.

DermaMNIST is a medical imaging dataset containing 7,000 images of skin lesions, each with a size of 224x224x3.

PathMNIST is a medical imaging dataset containing 9,000 images of breast cancer tissue, each with a size of 224x224x3.

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

Medicalsemi-supervisedsegmentationisatechniquewhereamodelistrainedtosegmentobjectsofinterestinmedicalimageswithlimitedannotateddata.

8,000 medical radiology images

30,000 medical radiology images

PACS repository archive of the Clinical Hospital Centre Rijeka

Robust segmentation of 104 anatomical structures in CT images.

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network.

Segment Anything in medical images.

Universal medical image segmentation (UniverSeg) method for few-shot segmentation of medical images.

Segment Anything (SAM) model for semantic segmentation of medical images.

A variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP) and SD models, trained exclusively on natural images, for solving the correspondence problems on medical images.

A hospital-scale chest X-ray database, namely “ChestX-ray8”, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight common disease...