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ShareScore release 0.9.0
Dataset results
2 results for “Conditional GAN”
pGAN Synthetic Dataset: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
<p>Synthetic dataset for <strong>A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs</strong></p> <p><strong> Dataset specification:</strong></p> <ul> <li>MRI images of Vertebral Units labelled based on region</li> <li>Dataset is comprised of 10000 pairs of images and labels</li> <li>Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]</li> <li>Images are 3D of size (9, 64, 64)</li> <li>Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar</li> </ul> <p>Arxiv paper: <a href="https://arxiv.org/abs/2106.13199">https://arxiv.org/abs/2106.13199</a><br> Github code: <a href="https://github.com/tcoroller/pGAN/">https://github.com/tcoroller/pGAN/</a></p> <p>Abstract:</p> <p>Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.</p>
Supplementary Materials for "Temperature field, flow field and temporal fluctuations thereof 2 in ammonothermal growth of bulk GaN – transition from dis-3 solution stage to growth stage conditions"
<p>Video S1: dissolution stage (upper part of nutrient and above), Video S2: 630 dissolution stage (baffle region), Video S3: dissolution stage (top seed), Video S4: dissolution stage 631 (middle seed), Video S5: dissolution stage (bottom seed), Video S6: growth stage (upper part of 632 nutrient and above), Video S7: growth stage (baffle region), Video S8: growth stage (top seed), Video 633 S9: growth stage (middle seed), Video S10: growth stage (bottom seed).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.