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979 results for “image dataset”
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>
Dataset of Instagram images related to Korean museums
<p>This dataset contains images crawled from Instagram using two hashtags related to two exhibitions held Seoul (South Korea) in 2020 and 2021. One is a generic hashtag for the Museum of Modern and Contemporary Art, MMCA, (#국립현대미술관), the other is for the "instagrammable" exhibition "Yumi's Cell Special Exhibition" (#유미의세포들특별전).</p> <p>The dataset contains more than 20,000 images (Image_all_*.zip).</p> <p>It also includes a selection of images in which there are humans interacting with objects and art installations: 9,409 (Yumi) and 810 (MMCA) (Image_Human_*.zip). The two archives Skeleton_*.zip contain the results of the skeleton analysis of such images, done with OpenPose.</p>
University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)
<p><strong>ABSTRACT </strong></p> <p>Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations. This dataset, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID), contains S-parameter measurements from experimental scans of MRI-derived breast phantoms, obtained with a pre-clinical breast microwave sensing system operating over 1-8 GHz. The dataset consists of measurements from over 1250 scans of a diverse array of phantoms. The phantom array consists of phantoms of various sizes and breast densities. The .stl files used to produce the 3D-printed phantoms are also included in the dataset. We hope that this dataset can serve as a resource for researchers in breast microwave sensing to evaluate signal processing, image reconstruction, and tumour detection methods.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Tyson Reimer, Jordan Krenkevich, Stephen Pistorius, June 16, 2021, "University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)", IEEE Dataport, doi: https://dx.doi.org/10.21227/1y0z-8t98.</p> <p>https://ieee-dataport.org/open-access/university-manitoba-breast-microwave-imaging-dataset-um-bmid</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Inverted microscopy image dataset -- Carbon biomass of microplankton assemblages in southern Patagonian fjords and channels
<p>Images of main microplanktonic items (folders) obtained under inverted (mostly) and electronic microscope used to estimate biovolume and carbon biomass. Scale bar is shown on each picture and label of each image indicate the station ID (St.) and sampling depth (m). A table is provided with biovolume and equivalent spherical diameter calculations for each planktonic item. </p>
Dataset related to article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging"
<p><em>The .zip file contains raw data related to the article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging", available from <a href="https://www.ijbs.com/v16p1363.htm">https://www.ijbs.com/v16p1363.htm</a>.</em></p> <ul> <li><em>The folder "<strong>fig 1 2 3 NIRA</strong>" contains raw data related to the experiments with niraparib presented in figures 1, 2 and 3 </em></li> <li><em>The folder "<strong>fig 1 2 3 OLA</strong>" contains raw data related to the experiments with olaparib presented in figures 1, 2 and 3 </em></li> <li><em>The folders "<strong>fig 4</strong>" and "<strong>fig 6</strong>" contain raw data related to figures 4 and 6, respectively.</em></li> </ul> <p><strong>For any additional information on how to read and reuse the dataset please contact Dr. Ubezio at paolo.ubezio@marionegri.it.</strong></p> <p> </p> <p><strong>ABSTRACT OF THE MANUSCRIPT:</strong></p> <p><strong>Rationale</strong>: Optimal intratumor distribution of an anticancer drug is fundamental to reach an active concentration in neoplastic cells, ensuring the therapeutic effect. Determination of drug concentration in tumor homogenates by LC-MS/MS gives important information about this issue but the spatial information gets lost. Targeted mass spectrometry imaging (MSI) has great potential to visualize drug distribution in the different areas of tumor sections, with good spatial resolution and superior specificity. MSI is rapidly evolving as a quantitative technique to measure the absolute drug concentration in each single pixel.</p> <p><strong>Methods</strong>: Different inorganic nanoparticles were tested as matrices to visualize the PARP inhibitors (PARPi) niraparib and olaparib. Normalization by deuterated internal standard and a custom preprocessing pipeline were applied to achieve a reliable single pixel quantification of the two drugs in human ovarian tumors from treated mice.</p> <p><strong>Results</strong>: A quantitative method to visualize niraparib and olaparib in tumor tissue of treated mice was set up and validated regarding precision, accuracy, linearity, repeatability and limit of detection. The different tumor penetration of the two drugs was visualized by MSI and confirmed by LC-MS/MS, indicating the homogeneous distribution and higher tumor exposure reached by niraparib compared to olaparib. On the other hand, niraparib distribution was heterogeneous in an ovarian tumor model overexpressing the multidrug resistance protein P-gp, a possible cause of resistance to PARPi.</p> <p><strong>Conclusions</strong>: The current work highlights for the first time quantitative distribution of PAPRi in tumor tissue. The different tumor distribution of niraparib and olaparib could have important clinical implications. These data confirm the validity of MSI for spatial quantitative measurement of drug distribution providing fundamental information for pharmacokinetic studies, drug discovery and the study of resistance mechanisms.</p>
Fused image dataset from light sheet microscope
<p>Example dataset that ships with VollSeg Napari samples, providing a 3D imaged dataset of fused Acadian embryo imaged with light sheet and fused over 4 angles.</p>
Accompanying dataset for: "IBEX: An iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues"
<p>These datasets were acquired using either the manual or automated IBEX multiplex imaging protocols and accompany the manuscript “IBEX: An iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues”, A. Radtke <em>et al.</em>, 2021, Nature Protocols.</p> <p>All image data are stored using the <a href="https://imaris.oxinst.com/support/imaris-file-format">Imaris file format</a>. To view these multi-channel images, you can either use one of these <strong>free</strong> viewers, <a href="https://imaris.oxinst.com/imaris-viewer">Imaris viewer</a>, <a href="https://imagej.net/Fiji">Fiji</a>.</p> <p>Each experiment has an associated imaging meta-data file in xlsx format and the resulting image in Imaris format.</p> <p><strong>Human Jejunum (Automated)</strong></p> <p>Dataset is a 24 parameter IBEX experiment performed on a human jejunum section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Kidney (Automated)</strong></p> <p>Dataset is a 16 parameter IBEX experiment performed on a human kidney Formalin-Fixed Paraffin-Embedded (FFPE) section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Lymph Node (Automated)</strong></p> <p>Dataset is a 25 parameter IBEX experiment performed on a human lymph node section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Skin (Automated)</strong></p> <p>Dataset is an 18 parameter IBEX experiment performed on a human skin section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Liver (Manual)</strong></p> <p>Dataset is a 22 parameter IBEX experiment performed on a human liver section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Human Lymph Node (Manual)</strong></p> <p>Dataset is a 38 parameter IBEX experiment performed on a human lymph node section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Human Spleen (Manual)</strong></p> <p>Dataset is a 25 parameter IBEX experiment performed on a human spleen section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p>
Molecule OCR Real images Dataset
<p>Test dataset from paper <strong>Image2SMILES: Transformer-based Molecular Optical Recognition Engine</strong>. The dataset contains 296 structures: images and Functional Groups SMILES (FG-SMILES). The structures were extracted from 24 papers, which were selected from each volume of Journal of Organic Chemistry (2020). </p>
Annotated datasets of Scots pine cross-sectional images for root rot detection and resin detection
<p>These are the annotated datasets used in the publication available at</p> <p><a href="https://doi.org/10.1080/14942119.2024.2327247">https://doi.org/10.1080/14942119.2024.2327247</a></p> <p>The first dataset is for root rot detection, the other dataset is for resin detection. Please see the publication for details.</p>
Vision Control Image Regression Dataset
<p>This is the final dataset of the bachelor thesis "Design, Train and Test an Image Regression Sensor" from the Carinthian University of Applied Sciences.</p> <p>https://github.com/LaurenzBeck/Vision-Control</p>
Dataset for "Effective super-resolution method for paired electron microscopic images"
<p>This is the image set used in the paper, Qian, Xu, Drummy, and Ding, 2020, “Effective super-resolution method for paired electron microscopic images,” <em>IEEE Transactions on Image Processing</em>, Vol. 29, pp. 7317–7330.</p>
Datasets for Evaluation of Multimodal Image Registration
<p><strong>Description</strong></p> <ul> <li><strong>Aerial data</strong></li> <li>The Aerial dataset is divided into 3 sub-groups by IDs: {7, 9, 20, 3, 15, 18}, {10, 1, 13, 4, 11, 6, 16}, {14, 8, 17, 5, 19, 12, 2}. Since the images vary in size, each image is subdivided into the maximal number of equal-sized non-overlapping regions such that each region can contain exactly one 300x300 px image patch. Then one 300x300 px image patch is extracted from the centre of each region. The particular 3-folded grouping followed by splitting leads to that each evaluation fold contains 72 test samples. <ul> <li> <p>Modality A: Near-Infrared (NIR)</p> </li> <li> <p>Modality B: three colour channels (in B-G-R order)</p> </li> </ul> </li> <li><strong>Cytological data</strong></li> <li>The Cytological data contains images from 3 different cell lines; all images from one cell line is treated as one fold in 3-folded cross-validation. Each image in the dataset is subdivided from 600x600 px into 2x2 patches of size 300x300 px, so that there are 420 test samples in each evaluation fold. <ul> <li> <p>Modality A: Fluorescence Images</p> </li> <li> <p>Modality B: Quantitative Phase Images (QPI)</p> </li> </ul> </li> <li><strong>Histological dataset</strong></li> <li>For the Histological data, to avoid too easy registration relying on the circular border of the TMA cores, the evaluation images are created by cutting 834x834 px patches from the centres of the original 134 TMA image pairs. <ul> <li> <p>Modality A: Second Harmonic Generation (SHG)</p> </li> <li> <p>Modality B: Bright-Field (BF)</p> </li> </ul> </li> </ul> <p>The evaluation set created from the above three publicly available 2D datasets consists of images undergone 4 levels of (rigid) transformations of increasing size of displacement. The level of transformations is determined by the size of the rotation angle θ and the displacement tx & ty, detailed in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets">this table</a>. Each image sample is transformed exactly once at each transformation level so that all levels have the same number of samples.</p> <ul> <li><strong>Radiological data</strong></li> <li>The Radiological dataset is divided into 3 sub-groups by patient IDs: {109, 106, 003, 006}, {108, 105, 007, 001}, {107, 102, 005, 009}. Since the Radiological dataset is non-isotropic (and also of varying resolution), it is resampled using B-spline interpolation to 1 mm<sup>3</sup> cubic voxels, taking explicit care to not resample twice; displaced volumes are transformed and resampled in one step. <ul> <li> <p>Modality A: T1-weighted MRI</p> </li> <li> <p>Modality B: T2-weighted MRI</p> </li> </ul> </li> </ul> <p>(Run <a href="https://github.com/MIDA-group/MultiRegEval/blob/master/utils/make_rire_patches.py"><code>make_rire_patches.py</code></a> to generate the sub-volumes.)</p> <p>Reference sub-volumes of size 210x210x70 voxels are cropped directly from centres of the (non-displaced) resampled volumes. Similarly as for the aforementioned 2D datasets, random (uniformly-distributed) transformations are composed of rotations θx, θy ∈ [-4, 4] degrees around the x- and y-axes, rotation θz ∈ [-20, 20] degrees around the z-axis, translations tx, ty ∈ [-19.6, 19.6] voxels in x and y directions and translation tz ∈ [-6.5, 6.5] voxels in z direction. 40 rigid transformations of increasing sizes of displacement are applied to each volume. Transformed sub-volumes, of size 210x210x70 voxels, are cropped from centres of the transformed and resampled volumes.</p> <p> </p> <p>In total, it contains 864 image pairs created from the aerial dataset, 5040 image pairs created from the cytological dataset, 536 image pairs created from the histological dataset, and metadata with scripts to create the 480 volume pairs from the radiological dataset. Each image pair consists of a reference patch <span class="math-tex">\(I^{\text{Ref}}\)</span> and its corresponding initial transformed patch <span class="math-tex">\(I^{\text{Init}}\)</span> in both modalities, along with the ground-truth transformation parameters to recover it.</p> <p>Scripts to calculate the registration performance and to plot the overall results can be found in <a href="https://github.com/MIDA-group/MultiRegEval">https://github.com/MIDA-group/MultiRegEval</a>, and instructions to generate more evaluation data with different settings can be found in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data">https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>In the <code>*.zip</code> files, each row in <code>{Zurich,Balvan}_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> or <code>Eliceiri_patches/patch_tlevel[1-4]/info_test.csv</code> provides the information of an image pair as follow:</p> <ul> <li> <p>Filename: identifier(ID) of the image pair</p> </li> <li> <p>X1_Ref: x-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y1_Ref: y-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X2_Ref: x-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y2_Ref: y-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X3_Ref: x-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y3_Ref: y-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X4_Ref: x-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y4_Ref: y-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X1_Trans: x-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y1_Trans: y-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X2_Trans: x-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y2_Trans: y-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X3_Trans: x-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y3_Trans: y-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X4_Trans: x-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y4_Trans: y-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Displacement: mean Euclidean distance between reference corner points and transformed corner points</p> </li> <li> <p>RelativeDisplacement: the ratio of displacement to the width/height of image patch</p> </li> <li> <p>Tx: randomly generated translation in the x-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>Ty: randomly generated translation in the y-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleDegree: randomly generated rotation in degrees to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleRad: randomly generated rotation in radian to synthesise the transformed patch I<sub>Init</sub></p> </li> </ul> <p>In addition, each row in <code>RIRE_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> has following columns:</p> <ul> <li>Z1_Ref: z-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></li> <li>Z2_Ref: z-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></li> <li>Z3_Ref: z-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></li> <li>Z4_Ref: z-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></li> <li>Z1_Trans: z-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></li> <li>Z2_Trans: z-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></li> <li>Z3_Trans: z-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></li> <li>Z4_Trans: z-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></li> <li>(...and similarly, coordinates of the 5th-8th corners)</li> <li>Tz: randomly generated translation in z-direction to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeX: randomly generated rotation around X-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadX: randomly generated rotation around X-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeY: randomly generated rotation around Y-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadY: randomly generated rotation around Y-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeZ: randomly generated rotation around Z-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadZ: randomly generated rotation around Z-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> </ul> <p> </p> <p><strong>Naming convention</strong></p> <ul> <li><strong>Aerial Data</strong> <ul> <li> <pre> zh{ID}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>zh5_03_02_R.png</code> indicates the <em>Reference </em>patch of the <em>3rd row</em> and <em>2nd column</em> cut from the image with ID <code>zh5</code>.</li> </ul> </li> <li><strong>Cytological data</strong> <ul> <li> <pre> {{cellline}_{treatment}_{fieldofview}_{iFrame}}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>PNT1A_do_1_f15_02_01_T.png</code> indicates the <em>Transformed </em>patch of the <em>2nd row</em> and <em>1st column</em> cut from the image with ID <code>PNT1A_do_1_f15</code>.</li> </ul> </li> <li><strong>Histological data</strong> <ul> <li> <pre> {ID}_{ReferenceOrTransformed}.tif</pre> </li> <li>Example: <code>1B_A4_T.tif</code> indicates the <em>Transformed </em>patch cut from the image with ID <code>1B_A4</code>.</li> </ul> </li> </ul> <ul> <li><strong>Radiological Data</strong> <ul> <li> <pre> patient_{ID}_{iTransform}_T.mhd</pre> </li> <li> <pre> patient_{ID}_R.mhd </pre> </li> <li>Example: <code>patient_003_8_T.mhd</code> indicates the sub-volume <em>Transformed </em>with the <em>8th random transformation</em> cut from the volume with patient ID <code>003</code>; <code>patient_003_R.mhd</code> indicates the <em>Reference </em>sub-volume the volume with patient ID <code>003</code>.</li> </ul> </li> </ul> <p> </p> <p>This dataset was originally produced by the authors of <em><a href="https://arxiv.org/abs/2103.16262">Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study</a></em>.</p>
DeepPlastic: An Open Source Image Dataset for Epipelagic Marine Plastic Detection
<p>Deep Plastic</p> <ul> <li>Enhanced Object Detection for Epipelagic Plastic.</li> <li>This repository contains source code for the method developed in <a href="https://arxiv.org/pdf/2105.01882.pdf">DeepPlastic: Identifying Marine Plastic In The Epipelagic Zone using Computer Vision and Deep Learning</a></li> <li> <p>Information:</p> </li> <li>Paper: [Coming Soon]</li> <li>YouTube video of Results: <a href="https://youtu.be/8zBdFxaK4Os">https://youtu.be/8zBdFxaK4Os</a></li> <li> <p>Object Detection Model</p> </li> <li>Four models: YOLOv4, YOLOv5, MobileSSD, Faster RCNN Inception V2</li> <li>Small efficient and high precision models can be used for real-time object detection.</li> <li>Model architecture and implementation details: <a href="https://arxiv.org/">https://arxiv.org/</a></li> <li>Weights for YOLOv4 and YOLOv5 are provided in the model/ <ul> <li>YOLOv4: best. weights; use <a href="https://drive.google.com/file/d/1YOTtZ2cHbqgxHukzLp01OVsUoa2CwwXs/view?usp=sharing">best.weights</a></li> <li>YOLOv5: best.pt; use <a href="https://drive.google.com/file/d/14mBOhtLrE2d3hudqjwBZmawKAvTF4zxS/view?usp=sharing">best.pt</a></li> </ul> </li> <li> <p>Google Colab Links</p> <p>Note: Click on File and Save Copy in Drive. If you try to edit my file it'll ask you for permission and send me an email. Please make your own copy.</p> </li> <li>YOLOv5: <a href="https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing">https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing</a></li> <li> <p>DeepTrash DataSet</p> </li> <li>1900 training images, 637 test images, 637 validation images (60, 20, 20 split)</li> <li>Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA.</li> <li>Deep Sea images are from JAMSTEK JEDI dataset: <a href="http://www.godac.jamstec.go.jp/">http://www.godac.jamstec.go.jp/</a></li> </ul>
Image Annotation Datasets
<p>This folder contains four Image Annotation Datasets (ESPGame, IAPR-TC12, ImageCLEF 2011, ImagCLEF 2012). Each dataset has sub-folders of training images, testing images, ground truth, labels.</p> <p>Moreover, labels are the limited number of labels the dataset could assign to an image. While the ground is the correct labeling for each image.</p>
MALDI imaging of mouse kidney peptides - test dataset
<p>This imzML test file is concise but meaningful as a training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>One 6 µm thick section of formalin-fixed paraffin-embedded mouse kidney (from 6 month old, male, C57 black 6 mice) was mounted onto an indium-tin oxide (ITO) glass slide, deparaffinized and subjected to antigen retrieval in citric acid (pH 6, 100°C, 1h). Before and after antigen retrieval the sample was washed with 10mM ammonium bicarbonate buffer. After air drying, four 1 µl spots of Bombesin (0.01 mg/ml) were placed around the tissue to control digestion.<strong> </strong>Trypsin was sprayed onto the tissue with the iMatrix Sprayer and the sample was incubated for 2 h at 50°C in a humid chamber. Internal Calibrants (Angiotensin I, Substance P, [Glu]-Fibrinopeptide B, ACTH 18-39) were mixed with α-Cyano-4-hydroxycinnamic acid (CHCA) matrix and sprayed onto the sample.</p> <p>The sample was measured with the Applied Biosystems/MDS SCIEX 4800 MALDI TOF/TOF™ Analyzer in reflector positive ion mode and a spatial resolution of 150 µm. The acquired Analyze7.5 file was loaded into Cardinal and filtered to reduce file size and decrease analysis time: Filtering was done for m/z values between 1220 and 1625 as well as pixel that represent about half of the kidney and one Bombesin digestion control spot. The data was exported in the common data format imzML.</p> <p> </p>
VegeNet - Image datasets and Codes
<p>Compilation of python codes for data preprocessing and VegeNet building, as well as image datasets (zip files).</p> <p>Image datasets:</p> <ol> <li><strong>vege_original</strong> : Images of vegetables captured manually in data acquisition stage</li> <li><strong>vege_cropped_renamed</strong> : Images in (1) cropped to remove background areas and image labels renamed</li> <li><strong>non-vege images</strong> : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods</li> <li><strong>food_image_dataset</strong> : Complete set of vege (2) and non-vege (3) images for architecture building.</li> <li><strong>food_image_dataset_split</strong> : Image dataset (4) split into train and test sets</li> <li><strong>process</strong> : Images created when cropping (pre-processing step) to create dataset (2). </li> </ol>
Pictures of diseased soybean leaves by category captured in field and with controlled backgrounds: Auburn soybean disease image dataset (ASDID)
<p>The dataset contains 2D images/photographs of diseased soybean leaves ideal for plant disease identification and visual object recognition research. Images were captured during the 2020 and 2021 soybean seasons using a Canon EOS 7D Mark II Digital SLR Camera and a Motorola Moto Z2 Play Smartphone from fields at the EV Smith Agricultural Research Station (Tallassee, Alabama), the Cullars Rotation (Auburn, Alabama), and the Brewton Agricultural Research Unit (Brewton, Alabama). Across both seasons there are a total of 9,981 original images collected across eight disease/deficiency categories. These include (1) healthy-looking plants, and those displaying the symptoms of (2) bacterial blight, (3) cercospora leaf blight, (4) downey mildew, (5) frogeye leaf spot, (6) soybean rust, (7) target spot, and (8) potassium deficiency. For each disease category, leaves were photographed at various canopy heights while still attached to the plant in the field or they were detached from the plant and then immediately photographed while laid flat on the ground in trimmed grass or on a white surface. Images were collected with the goal of developing a Convolutional Neural Network (CNN)-based automated classifier of digital images of soybean diseases. Dataset is well-suited for classification modeling.</p>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Dataset literatul reviuw on Cyberlaw OR Cyber Law AND image splicing
<p>Ini merupakan dataset dari artikel dataset literatul reviuw on Cyberlaw OR Cyber Law AND image splicing</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></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.