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Dataset results
9 results for “Mask Detection”
Dataset: Mask R-CNN Based C. Elegans Detection with a DIY Microscope
<p>The dataset consists of images of C. elegans in Petri Dish that were captured at a frequency of 1 Hz at 3280 × 2464 pixels via a Raspberry Pi based DIY Microscope. Further details of the recording setup and the dataset can be found in the corresponding article.</p> <p>Up on use, please cite the following article <a href="https://doi.org/10.3390/bios11080257">https://doi.org/10.3390/bios11080257</a> such as:</p> <p>Fudickar, S.; Nustede, E.J.; Dreyer, E.; Bornhorst, J. Mask R-CNN Based C. Elegans Detection with a DIY Microscope. <em>Biosensors</em> <strong>2021</strong>, <em>11</em>, 257. https://doi.org/10.3390/bios11080257</p> <p> </p> <p><br> </p>
Mask Detection Application Performance Models
<p>The repository includes the source code and the datasets used to build performance models for OSCAR Mask Detection application. The results are included in the AI-SPRINT project deliverable "D2.1 - First release and evaluation of the AI-SPRINT design tools".</p>
Face mask detection and masked facial recognition dataset (MDMFR Dataset)
<p>The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0170">MDMFR, 2022</a>) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, <a href="https://www.sciencedirect.com/topics/computer-science/illumination-condition">illumination conditions</a>, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0215">Plastiliq, 2022</a>).</p>
Figure S1: Tissue detection in StrataQuest software via creation of a digital mask; Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding.
<p><strong>Figure S1: Tissue detection in StrataQuest software via creation of a digital mask</strong><strong>.</strong> A digital ‘mask’ is created to measure the area of tissue for quantification of cell densities. The tissue mask is automatically generated by StrataQuest software via conversion of the scanned image from RGB to grayscale and then application of an intensity threshold. Manual adjustments are made to the tissue mask to remove necrotic areas and/or staining artefacts. Only nuclei present within the tissue mask are quantified. Representative tumour cores with low (A) and high (B) SOX2 densities, respectively, with corresponding overlaid tissue masks are shown (C-D; purple colour). Tissue mask generation is based on haematoxylin staining so is not affected by the level of DAB staining. Scale bars 200µm.</p> <p><strong>Figure S2: StrataQuest workflow for detection of SOX2+ nuclei and thresholding. </strong>Inbuilt colour deconvolution algorithms within StrataQuest software separate SOX2 DAB (brown) staining from haematoxylin (blue) staining to produce grayscale images for each channel. Nuclear segmentation was then performed on the resulting grayscale DAB image to detect brown-stained nuclei. This is possible as SOX2 expression is localised to the nucleus. Segmented nuclear masks overlaid onto the DAB (brown) channel (A) and the original colour image (B) allow visualisation of the segmentation algorithm. The software then calculates parameters such as nuclear size, haematoxylin intensity and DAB intensity for each nuclear mask, which are reported on a scattergram, with each dot representing a single nuclear mask. A scattergram displaying DAB intensity vs haematoxylin intensity is used to threshold and accurately detect SOX2+ stained nuclei (C). Gated nuclei from the scattergram (C) can be visualised on an image of segmented nuclear masks overlaid onto the RGB image (D). Red nuclei represent SOX2+ cells (red gate on C) and green nuclei are SOX2- cells (green gate on C). Scale bars 50μm.</p>
SARS-COV-2 Detection From Used Surgical Mask
ClinicalTrials.gov study NCT06027398. IPD Sharing: YES. Countries: 1. Publications: 4.
Data from: Masking of an auditory behaviour reveals how male mosquitoes use distortion to detect females
Open the record for dataset details and reuse information.
Investigation of the Accordance Between Event Detection of prismaLINE Devices and Polysomnography Using Full Face Masks
ClinicalTrials.gov study NCT06317077. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Face Mask Detection Dataset
<p>The dataset contains real images of people with face mask and without face mask. Each class contains 150 images.</p> <p>Cite us:</p> <p>Ferdib-Al-Islam, Suprio Sarkar, Nusrat Jahan and Farjana Yeasmin Rupa, “Face Mask Detection Dataset”. Zenodo, Jul. 15, 2021. doi: 10.5281/zenodo.5305989</p>
The Study Will Enroll Females Who Are Coming in for Their Annual Mammogram Screening and Who Are Schedule for a Biopsy to Provide a Breath Sample Via a Mask to Evaluate if Breast Cancer Can be Detecte
ClinicalTrials.gov study NCT07266194. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ScienceDex guides
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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.