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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.
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
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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 ...
The portal for medical data models is a German [1] and European [2] medical research infrastructure. It is an open-access metadata-repository initiated for scientific purposes that can generate, analyse, release and reuse medical forms.
The full original dataset is referred to as ImageNet-21K. ImageNet-21k contains 14,197,122 images divided into 21,841 classes. Some papers round this up and name it ImageNet-22k. [29] The full ImageNet-21k was released in Fall of 2011, as fall11_whole.tar. There is no official train-validation-test split for ImageNet-21k.
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.
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 ]