91 datasets found

Groups: Medical Image Analysis

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  • EyeQ

    Retinal fundus images have been extensively used for clinical analysis of various ocular diseases, but the quality of fundus images is critical to the diagnosis and screening of...

  • LesionPaste: One-Shot Anomaly Detection for Medical Images

    LesionPaste is a one-shot anomaly detection framework for medical images that utilizes true anomalies from a single sample and synthesizes artificial anomalous samples.

  • Osteoarthritis Initiative (OAI) dataset

    Knee OsteoArthritis (KOA) dataset used for early detection of KOA (KL-0 vs KL-2) using Vision Transformer (ViT) model with selective shuffled position embedding and key-patch...

  • DRIVE

    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.

  • Head CT scans dataset

    The head CT scans dataset is used for training and evaluation of the proposed method.

  • CQ500 dataset

    The CQ500 dataset is a head CT scans dataset used for training and evaluation of the proposed method.

  • HISTO-FED

    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...

  • Evaluation framework for algorithms segmenting short axis cardiac MRI

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

  • SI-170 dataset

    The dataset used in this paper is a collection of T1, T2, and T2-FLAIR images acquired at a 3T SIEMENS scanner from 170 MS patients.

  • GE-30 dataset

    The dataset used in this paper is a collection of T1, T2, and T2-FLAIR images acquired at a 3T GE scanner from 30 MS patients.

  • MESSIDOR

    The dataset is used for simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network.

  • DRIONS-DB

    The dataset is used for simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network.

  • DiaRetDb1

    The dataset is used for simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network.

  • e_ophtha_EX

    The dataset is used for simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network.

  • GlaS dataset

    The GlaS dataset contains 165 H&E-stained histopathology patches extracted from 16 WSIs.

  • Breast Cancer

    A neural network with single-hidden layer of 64 hidden units and ReLU activations. A prior precision of ε = 1, a minibatch size of 128 and 16 Monte-Carlo samples are used for...

  • UMN dataset

    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...

  • KERMANY dataset

    The KERMANY dataset contains 256 OCT scans from DME patients.

  • OPTIMA dataset

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

  • OPTIMA, KERMANY, and UMN datasets

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