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48 results for “Synthetic Images”
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>
Parcel3D - A Synthetic Dataset of Damaged and Intact Parcel Images with 2D and 3D Annotations
<p>Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and for visual samples our <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p><br> Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>classification</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction</li> <li>3D reconstruction</li> </ul> <p> </p> <p>The dataset is for <strong>academic research use only</strong>, since it uses resources with restrictive licenses.<br> For a detailed description of how the resources are used, we refer to our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p>Licenses of the resources in detail:</p> <ul> <li><a href="https://research.google/resources/datasets/scanned-objects-google-research/">Google Scanned Objects</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (for details on which files are used, see the respective <em>meta </em>folder)</li> <li><a href="https://zenodo.org/record/8041823">Cardboard Dataset</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/8999123">Shipping Label Dataset</a>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></li> <li>Other Labels: See file <em>misc/source_urls.json</em></li> <li><a href="https://github.com/weberhen/learning_indoor_lighting">LDR Dataset</a>: License for Non-Commercial Use</li> <li><a href="https://data.vision.ee.ethz.ch/sagea/lld/">Large Logo Dataset (LLD)</a>: Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.</li> </ul> <p>You can use our textureless models (i.e. the <em>obj</em> files) of damaged parcels under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (note that this does not apply to the textures).</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }</code></pre>
Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
Open the record for dataset details and reuse information.
Image and diffractions of a Synthetic-holographic protein
<p>In this dataset are collected the image plane and diffraction patterns of phase Synthetic-Computer generated hologram of a ferritin protein.</p> <p>We designed such holograms to test our OAM sorting system for our future studies on proteins</p>
Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
<p>Biological systems inspire the design of multifunctional materials and devices. However, current syynthetic replicas rarelyy capture the range of structural complexityy observed in natural materials. Prior to the definition of a biomimetic design, a dual investigation with a common set of criteria for comparing the biological material and the replica is required. Here, we deal with this issue by addressing the non-trivial case of insect cuticles tessellated with polygonal microcells with iridescent colors due to the twisted cholesteric organization of chitin fibers. By using hyperspectral imaging within a common methodology, we compare, at several length scales, the textural, structural and spectral properties of the microcells found in the two-band cuticle of the scarab beetle <i>Chrysina gloriosa</i> with those of the polygonal texture formed in flat films of cholesteric liquid crystal oligomers. The hyperspectral imaging technique offers a unique opportunity to reveal the common features and differences in the spectral-spatial signatures of biological and synthetic samples at a 6-nm spectral resolution over 400 nm-1000 nm and a spatial resolution of 150 nm. The biomimetic design of chiral tessellations is relevant to the field of non-specular properties such as deflection and lensing in geometric phase planar optics.</p>
SIDIRE: Synthetic Image Dataset for Illumination Robustness Evaluation
<p>SIDIRE is a freely available image dataset which provides synthetically generated images allowing to investigate the influence of illumination changes on object appearance. The images are renderings of 3D coin models with different material BRDFs and levels of texturedness. Thus, the dataset makes it possible to directly evaluate the influence of these conditions on the performance of image recognition without introducing a bias due to different objects used between image sets. The dataset has been used for evaluation in [1].</p> <p><strong>Usage</strong></p> <p>The dataset is freely available for non-commercial research use. Please cite our paper [1] when using the dataset for your research.</p> <p><strong>Technical Details</strong></p> <p>Full Image Dataset</p> <p>The full image dataset consists of images of 14 coin models which have been rendered using the open-source graphics software <a href="http://www.blender.org">Blender</a>. For each model, twelve sets of 500×500 images with 65 illumination directions were rendered where each set represents one out of four material BRDFs and one out of three texture density levels. Material BRDFs are intended to represent different levels of specularity starting from a Lambertian material with zero specularity up to specular intensity values of 0.25, 0.50 and 1.00. The first texture density level shows no texture and thus represents the set of textureless objects. For the remaining two levels synthetically generated textures were used. The camera image plane is placed parallel to the coin and light source positions are defined by their azimuth angle φ and elevation angle λ. We used eight levels of λ with eight levels of φ each to produce 64 images. The 65th image is rendered with the light placed at the camera position (i.e. λ=90°).<br> In the provided RAR-file, all the 65 images of a specific model, specularity level and texturedness level are contained in separate directories. For instance, the directory ‘texture_level0\Ref_level2\2874-back’ contains the images of the model ‘2874-back’ rendered without texture and a specularity of 0.50.</p> <p><strong>Patch Dataset</strong></p> <p>The patch dataset contains 50000 matching patch pairs for every of the 12 subsets of SIDIRE. It can be used to generate groups of feature distances by means of true and false patch pairs, in the same manner as, e.g., Matthew Brown’s <a href="http://phototour.cs.washington.edu/patches/default.htm">patch dataset</a>. Please see [1,2] for a detailed description of the evaluation scheme of patch pair databases.<br> The patches have a size of 64×64 and are arranged in images of size 3200×3200. Thus, every image contains 2500 patches where corresponding patches are placed side by side. The patches of the 12 subsets are contained in directories indicating their texture density and reflectance level, e.g. patches rendered without texture and a specularity of 0.50 are contained in the directory ‘tex0_ref2’.<br> </p> <p><strong>References</strong></p> <p>[1] Zambanini S., Kampel M. “Evaluation of Low-Level Image Representations for Illumination-Insensitive Recognition of Textureless Objects”, <em>International Conference on Image Analysis and Processing – ICIAP’13</em>, Naples, Italy, September 2013. (<a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13.pdf">pdf</a>, <a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13_supp1.pdf">supplementary material</a>)<br> [2] Brown, M., Gang Hua, Winder, S., “Discriminative Learning of Local Image Descriptors”, <em>Pattern Analysis and Machine Intelligence, </em> vol.33, no.1, pp.43-57, 2011.</p>
Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain
<p>This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).<br>This dataset is associated with the following paper:</p> <p><strong>- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.</strong> Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566</p> <p>We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.</p> <p><strong>Work using any of these data should cite the following references:</strong></p> <ul> <li>Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566</li> <li>Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427</li> <li>Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094</li> </ul> <p>This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).</p> <p>Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.</p> <p>Note: <em>Terms of use for the original cohort (</em>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021<em>) are for research and education purposes only.</em></p>
Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data
<p>Data accompanying our paper on: <em>Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data</em></p> <p> </p> <p><a href="https://github.com/tlpss/diffusing-synthetic-data" target="_blank" rel="noopener">github repository</a></p> <p>meshes.zip contains the 3D meshes used to generate the synthetic data for all three object categories</p> <p>real-datasets.zip contains the real-world image datasets gathered to evaluate the synthetic data</p> <p> </p> <p> </p>
DeepCAD-RT dataset: synthetic calcium imaging data
<p>DeepCAD-RT dataset: synthetic calcium imaging data</p>
(SEN12MS) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>SEN12MS </strong>NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(capsicum) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>capsicum</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p>
(nirscene) deepNIR: Dataset for generating synthetic NIR images
<p>This dataset contains <strong>nirscene</strong> NIR+RGB dataset used in our paper; deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques.</p> <p>Please refer to <a href="http://tiny.one/deepNIR">http://tiny.one/deepNIR</a> for more detail.</p> <p> </p>
Synthetic High-Voltage Power Line Insulator Images
<p>This database contains Synthetic High-Voltage Power Line Insulator Images.</p> <p>There are two sets of images: one for image segmentation and another for image classification.</p> <p>The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.</p> <p>The second file contains synthetic that simulate the most common impurities found on high-voltage transmission line insulator strings: salt, volcanic soot, bird excrement and a clean insulator. Each type of dirt has 3,608 images, with 1,202 images for each type of insulator material, with a total of 14,432 images.</p>
Synthetic images of fluorescent spots and ground truth data
<p>Synthetical images of fluorescent spots and ground truth data created with the simcep software.</p>
Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification
<p>We uploaded the dataset of included patients of manuscript: Petrillo A, Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rosaria Rubulotta M, Setola SV, Mattace Raso M, Maio F, Raiano C, Siani C, Di Bonito M, Botti G. Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification. Breast J. 2020 May;26(5):860-872. doi: 10.1111/tbj.13739. Epub 2019 Dec 30. PMID: 31886607.</p>
Synthetic Aperture Anomaly Imaging
<p><strong>Abstract: </strong>Previous research has shown that in the presence of foliage occlusion, anomaly detection performs significantly better in integral images resulting from synthetic aperture imaging compared to applying it to conventional aerial images. In this article, we hypothesize and demonstrate that integrating detected anomalies is even more effective than detecting anomalies in integrals. This results in enhanced occlusion removal, outlier suppression, and higher chances of visually as well as computationally detecting targets that are otherwise occluded. Our hypothesis was validated through both: simulations and field experiments. We also present a real-time application that makes our findings practically available for blue-light organizations and others using commercial drone platforms. It is designed to address use-cases that suffer from strong occlusion caused by vegetation, such as search and rescue, wildlife observation, early wildfire detection, and surveillance.</p>
Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
Open the record for dataset details and reuse information.
Synthetic histology images of colorectal cancer, generated by conditional generative adversarial networks
<p>These are generated (synthetic) histology images of colorectal cancer. These images were generated by conditional GANs and are in two classes: MSIH (microsatellite instable high) and nonMSIH. There are two sets: one set with 10K images per class and another one with 75K images per class. All images are RGB, 512x512 px at a resolution of 0.5 micrometers per pixel. For more information, please stay tuned for our upcoming manuscript on www.kather.ai. </p>
Comparative Study of Conventional 1.5 and 3.0T MR Images With Synthetically Reconstructed MR Images
ClinicalTrials.gov study NCT02596854. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparative Study of Conventional MR Images With Synthetically Reconstructed MR Images of the Brain
ClinicalTrials.gov study NCT05425927. IPD Sharing: NO. Countries: 1. Publications: 0.
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.