1. Classification of optical coherence tomography images using a capsule network

    Classification of optical coherence tomography images using a capsule network

    Background Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network. Methods From an OCT dataset, we produced a training dataset of 83,484 images and a test ...

    Read Full Article

    Login to comment.

  1. Categories

    1. Applications:

      Art, Cardiology, Dentistry, Dermatology, Developmental Biology, Gastroenterology, Gynecology, Microscopy, NDE/NDT, Neurology, Oncology, Ophthalmology, Other Non-Medical, Otolaryngology, Pulmonology, Urology
    2. Business News:

      Acquisition, Clinical Trials, Funding, Other Business News, Partnership, Patents
    3. Technology:

      Broadband Sources, Probes, Tunable Sources
    4. Miscellaneous:

      Jobs & Studentships, Student Theses, Textbooks
  2. Authors