91 datasets found

The DRIVE dataset includes 40 color fundus photographs divided into two parts: 20 training images and 20 test images with manual segmentation of the vessels and binary masks of...

Medical images, captions, and textual references

A dataset for polyp segmentation and neoplasm detection in colonoscopy

Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an...

Intracranial hemorrhage segmentation using a deep convolutional model

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,...

Surgical instrument segmentation aims to accurately identify and delineate surgical instruments in operative scenes.

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

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

3D MRI brain tumor segmentation using deep convolutional neural networks.

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...

Lung organ segmentation dataset

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 abdominal CT scans used for the Medical Out-of-Distribution (MOOD) challenge

The HCP30 dataset used for the Medical Out-of-Distribution (MOOD) challenge

The dataset used for the Medical Out-of-Distribution (MOOD) challenge

ExplainFix adopts two design principles: the “fixed filters” principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never...

The evaluation of prostate segmentation algorithms for mri challenge dataset

The computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri dataset