Biomedical and Health Sciences

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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  • Biomedical and Health Sciences
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    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.

    1043 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38

    This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

    The DataPort Repository contains the data used primarily for generating Figure 1.

    545 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Sat, 06/16/2018 - 23:05

    Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

  • Artificial Intelligence
  • Computer Vision
  • Image Processing
  • Machine Learning
  • Biomedical and Health Sciences
  • Medical Imaging
  • Last Updated On: 
    Wed, 01/15/2020 - 21:42

    Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

    This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

    This dataset relates to 718 of the 1012 LIDC-IDRI scans.

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  • Artificial Intelligence
  • Last Updated On: 
    Tue, 01/14/2020 - 10:53

     

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  • Biomedical and Health Sciences
  • Last Updated On: 
    Sat, 01/18/2020 - 21:54

    For more information please take a look at the corresponding paper (DOI: 10.1109/JBHI.2019.2963786)

    31 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Mon, 01/13/2020 - 08:15

    This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.

    29 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Fri, 01/10/2020 - 04:22

    This dataset contains light-field microscopy images and converted sub-aperture images. 

     

    The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.

     

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  • Image Processing
  • Last Updated On: 
    Thu, 01/09/2020 - 12:42

    The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing.

    38 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Wed, 01/08/2020 - 05:45

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