This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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MATLAB code for the proposed Single-shot Super-Resolution Phase Retrieval (SSR-PR) algorithm.

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Optical coherence tomography (OCT) is a powerful technology for monitoring and diagnosing eye diseases. However, speckle noise is not beneficialfor improving OCT image quality and further image analysis,such as segmentation of the retinal layer.Inspired by the rapid development of deep learning, several methods have been proposed for OCT denoising, and promising results have been obtained.

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This dataset includes two 3D models in .stl format:

- a reference frame (RF) for surgical navigation. It contains three branches for the attachment of spherical (passive) optical markers.

- a socket for the insertion of the RF. The socket model can be added to the design of a surgical guide.

These models were designed to be 3D printed with a Formlabs Form 2 3D printer in resin.

Instructions: 

Download the models and add the socket model to the design of your surgical guide. Then print the reference frame and the socket + surgical guide. These models were designed to be 3D printed in resin using a Formlabs Form 2 3D printer. If a different material or printer is used, adjustments may be necessary to achieve a correct insertion. Thoroughly remove all supports.

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The University of Turin (UniTO) released the open-access dataset Stoke collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP).

Instructions: 

Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.

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229 Views

The region-based segmentation approach has been a major research area for many medical image applications. A vision guided autonomous system has used region-based segmentation information to operate heavy machinery and locomotive machines intended for computer vision applications. The dataset contains raw images in .png format fro brain tumor in various portions of brain.The dataset can be used fro training and testing. Images are calssified into three main regions as frontal lobe(level -1, level-2), optus-lobe(level-1), medula_lobe(level-1,level-2,level-3).

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The dataset contains 236 X-ray images, all of which include the top of the head to the middle of the thigh. The included patients are 18-80 years old, and treated to the department of orthopedics due to low back pain or spinal deformity. The original size of the X-ray images is around 3000×7000px.

 

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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.

Instructions: 

The University of Manitoba Breast Microwave Imaging Dataset (UM-BMID) isan open-access dataset available to all researchers. The dataset containsdata from experimental scans of MRI-derived breast phantoms.The dataset itself can be found at https://bit.ly/UM-bmid. The complete documentation for the dataset is also available at this link.

A GitHub page associated with the dataset can be found here: https://github.com/UManitoba-BMS/UM-BMID.The dataset is described in an accepted manuscript:T. Reimer, J. Krenkevich, and S. Pistorius, "An open-access experimentaldataset for breast microwave imaging,", in _2020 European Conference onAntennas and Propagation (EuCAP 2020)_, Copenhagen, Denmark, Mar. 2020,pp. 1-5, doi:10.23919/EuCAP48036.2020.9135659.This GitHub repository (https://github.com/UManitoba-BMS/UM-BMID) contains the code used to produce the resultspresented in that paper and supportive scripts for the UM-BMID dataset.

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The dermoscopic images considered in the paper "Dermoscopic Image Classification with Neural Style Transfer" are available for public download through the ISIC database (https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main). These are 24-bit JPEG images with a typical resolution of 768 × 512 pixels. However, not all the images in the database are in satisfactory condition.

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181 Views

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