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This collection of medical image datasets is a valuable resource for anyone involved in medical imaging and disease research. It includes a variety of images from different medical fields, all designed to support research in diagnosis and treatment. The datasets cover chest CT-scans, lung radiography, brain MRI, retinal imaging, and gastrointestinal tract imaging. The chest CT-scan dataset includes 867 images of normal lungs and three types of lung cancer—adenocarcinoma, large cell carcinoma, and squamous cell carcinoma—providing essential data for understanding lung cancer.
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We evaluated the strategy performance on three different datasets (MNIST, FMNIST, and CIFAR10), which is simulated heterogeneity by assigning different data volume labels to these datasets. These datasets all consist of image data for vehicle perception tasks. The MNIST dataset contains 70,000 images from 10 different classes, including 60,000 train and 10,000 test samples . FMNIST and MNIST have similar data structures, both are grayscale images. In contrast, FMNIST focuses on more complex target recognition tasks, which contains 10 categories of everyday items .
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Despite advances in vascular replacement and repair, fabricating small-diameter vascular grafts with low thrombogenicity and appropriate tissue mechanics remains a challenge. A wide range of platforms have been developed to use plant-derived scaffolds for various applications. Unlike animal tissue, plants are primarily composed of cellulose which can offer a promising, nonthrombogenic alternative capable of promoting cell attachment and redirecting blood flow.
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This dataset contains source images of TPAMI3274826 (Fig. 10, Fig. 11, Fig. 13).
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this is dataset for our paper: "Large-scale Benchmark for Uncooled Infrared Image Deblurring", submitted for IEEE SIgnal Processing Letters.
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We use industrial cameras to take images of steel wire ropes under different conditions, and use these wire rope images to train the U_Net network, and realize the semantic segmentation of the wire rope images by the U_Net network.
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This dataset was created for Deformable Linear Objects(DLOs) segmentation and crossings classification under complex background.
RGB images, overlap maps, gradient maps are included for segmentation task. The quantity of DLOs range from one to three.
Upper and lower type of crossings are defined for classification task.
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34 Views