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8 results for “gigapixel”
Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis
<p>This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.</p> <p>The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:<br> * id: unique identifier within each partition.<br> * tilted_label: image-level label corresponding to the tilted rectangle.<br> * nontilted_label: image-level label corresponding to the non-tilted rectangle.<br> * tilted_size: longest size of the tilted rectangle.<br> * nontilted_size: longest size of the non-tilted rectangle.<br> * kind: either "tile" or "mask" image type.</p> <p>The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".</p> <p>The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.</p>
Tutorial and dataset for gigapixel-like imaging strategies for dental anthropology
<p>This tutorial and image dataset to accompany the following publication: Willman JC, Lozano M, Hernando R, Vergès JM. Gigapixel-like imaging strategies for dental anthropology: Applications for scientific communication and training in digital image analysis. Quaternary International, <a href="https://doi.org/10.1016/j.quaint.2020.05.027">https://doi.org/10.1016/j.quaint.2020.05.027</a>. Part of the Special Issue: Not Only Use.</p> <p><strong>Contains: </strong>tutorial,<strong> </strong>183 images files for reconstructing three examples of gigapixel-like images, and one “READ ME” file describing the images.</p> <p>The tutorial is meant to be used as a guideline for the creation of gigapixel-like (GPL) images of dental surfaces based on our experience. The methodology can be extrapolated to other types of materials and surfaces, but you may need to augment these guidelines according to the specificity of your own research needs. We hope that the inclusion of this supplement will stimulate other researchers to include specific guidelines and step-by-step processes for how they created their own GPL images. While this study concentrates on scanning electron microscopy (SEM) images, there are many other ways to acquire two-dimensional images (e.g., digital photography, optical light microscopy, etc.) that can be used to create GPL images. Likewise, the number of software packages and their numerous built-in parameters for creating extended focus and mosaic images vary greatly. Therefore, more tutorials/guidelines will surely improve the transparency and accessibility of the GPL methodology in the archaeological sciences, biological anthropology, and allied fields.</p>
IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup
<p>Mouse pup data (Xenium, main results):</p> <ol> <li>Mouse.zip: Training data</li> <li>dapi_raw.tif: The raw dapi WSI</li> <li>dapi_gen.tif: The generated dapi WSI by IST-editing</li> <li>he_raw.tif: The raw H&E WSI</li> <li>he_gen.tif: The generated H&E WSI by IST-editing</li> </ol> <p>Mouse brain data (Vizgen, supplementary results):</p> <ol> <li>brain_60988.zip: Training data from two brain sections </li> <li>609882_raw.tif: The raw dapi WSI for mouse with ID 609882</li> <li>609882_trn.tif: The generated WSI on the training brain section</li> <li>609882_tst.tif: The generated WSI on the test brain section</li> <li>609889_raw.tif: The raw dapi WSI for mouse with ID 609889</li> <li>609889_trn.tif: The generated WSI on the training brain section</li> <li>609889_tst.tif: The generated WSI on the test brain section</li> </ol> <p> </p> <p> </p> <p> </p>
Source data for VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images publication
<p>Source data used to create figures in <em>VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images</em> (Nature Communications, 2023)</p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Data from: Gigapixel big data movies provide cost‐effective seascape scale direct measurements of open‐access coastal human use such as recreational fisheries
Collecting data on unlicensed open‐access coastal activities, such as some types of recreational fishing, has often relied on telephone interviews selected from landline directories. However, this approach is becoming obsolete due to changes in communication technology such as a switch to unlisted mobile phones. Other methods, such as boat ramp interviews, are often impractical due to high labor cost. We trialed an autonomous, ultra‐high‐resolution photosampling method as a cost effect solution for direct measurements of a recreational fishery. Our sequential photosampling was batched processed using a novel software application to produce "big data" time series movies from a spatial subset of the fishery, and we validated this with a regional bus‐route survey and interviews with participants at access points. We also compared labor costs between these two methods. Most trailer boat users were recreational fishers targeting tuna spp. Our camera system closely matched trends in temporal variation from the larger scale regional survey, but as the camera data were at much higher frequency, we could additionally describe strong, daily variability in effort. Peaks were normally associated with weekends, but consecutive weekend tuna fishing competitions led to an anomaly of high effort across the normal weekday lulls. By reducing field time and batch processing imagery, Monthly labor costs for the camera sampling were a quarter of the bus‐route survey; and individual camera samples cost 2.5% of bus route samples to obtain. Gigapixel panoramic camera observations of fishing were representative of the temporal variability of regional fishing effort and could be used to develop a cost‐efficient index. High‐frequency sampling had the added benefit of being more likely to detect abnormal patterns of use. Combinations of remote sensing and on‐site interviews may provide a solution to describing highly variable effort in recreational fisheries while also validating activity and catch.
Data from: Gigapixel big data movies provide cost‐effective seascape scale direct measurements of open‐access coastal human use such as recreational fisheries
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Generating highly accurate pathology reports from gigapixel whole slide images with HistoGPT
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.