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Aerial Image Segmentation Dataset 80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0. Images manually segmented. 80 Images Aerial Classification, object detection 2013 [156] [157] J. Yuan et al. KIT AIS Data Set Multiple labeled training and evaluation datasets of aerial images of crowds.
Has there been any consideration of adding medical imaging datasets? There are some large public repositories of medical images that could be very useful to people looking for ML data such as The Cancer Imaging Archive, which has radiology and histopathology imaging data from many thousands of subjects along with extensive supporting data that could be used as labels for common classification ...
Award-winning free collaboratory with over 1000 neuroinformatics software tools, imaging datasets, and community resources including forums and events. Human, mouse, rat, other Microscopic, macroscopic Datasets Healthy and diseased: No Open Access Series of Imaging Studies (OASIS) Structural MRI images Human Macroscopic MRI datasets
The Cancer Imaging Archive (TCIA) is an open-access database of medical images for cancer research. The site is funded by the National Cancer Institute's (NCI) Cancer Imaging Program, and the contract is operated by the University of Arkansas for Medical Sciences. Data within the archive is organized into collections which typically share a ...
LabelMe is a project created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that provides a dataset of digital images with annotations. The dataset is dynamic, free to use, and open to public contribution. The most applicable use of LabelMe is in computer vision research. As of October 31, 2010, LabelMe has 187,240 ...
MICC-Flickr 101 is an image data set created at the Media Integration and Communication Center (MICC), University of Florence, in 2012. It is based on Caltech 101 and is collected from Flickr . MICC-Flickr 101 [ 16 ] corrects the main drawback of Caltech 101, i.e. its low inter-class variability and provides social annotations through user tags.
The Overhead Imagery Research Data Set (OIRDS) is a collection of an open-source, annotated, overhead images that computer vision researchers can use to aid in the development of algorithms. [1] Most computer vision and machine learning algorithms function by training on a large set of example data. [ 2 ]
Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues . Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease.