NYU Data Catalog

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Results Found: 3
  • NYU Dataset

    fastMRI

    Authors
    Florian Knoll
    Patricia M. Johnson
    Daniel K. Sodickson
    Michael P. Recht
    1 more author(s)...
    Description

    This deidentified imaging dataset is comprised of raw k-space data in several sub-dataset groups. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMRD format and the RSNA Clinical Trial Processor, respectively. Manual inspection of each DICOM image was also performed to check for the presence of any unexpected protected health information (PHI), with spot checking...

    Subject
    Anatomy
    Cancer
    Access Rights
    Free to All
    Application Required
  • NYU Dataset

    One Hundred Knee MRI Cases

    Authors
    Kerstin Hammernik
    Teresa Klatzer
    Erich Kobler
    Michael P. Recht
    3 more author(s)...
    Description

    This dataset includes one hundred knee MRI cases. The one hundred cases comprise five 20-case sets for each of the following sequences: Coronal spin density weighted with fat suppression, Coronal spin density weighted without fat suppression, Azial Ts weighted with fat suppression, Saggittal T2 weighted with fat suppression, Sagittal spin density weighted. The dataset is organized slice by slice,...

    Subject
    Anatomy
    Access Rights
    All NYU
  • NYU Dataset

    Radiologist and Deep Neural Network Predictions for Low-pass Filtered Mammograms

    Authors
    Taro Makino
    Stanisław Jastrzębski
    Witold Oleszkiewicz
    Celin Chacko
    17 more author(s)...
    Description

    Investigators manipulated images from the NYU Breast Cancer Screening Dataset to identify differences in the the features of perception used in diagnosis by radiologists versus deep neural networks (DNNs). Two studies were conducted. In the reader study, a set of 720 exams were processed with Gaussian low-pass filtering at varying severity levels and ten radiologists and five DNNs (trained on unperturbed...

    Subject
    Cancer
    Access Rights
    Free to All