Computer Vision

Glaucoma is the leading cause of irreversible blindness in the world, and primary angle closure glaucoma (PACG) is one of the main subtypes. PACG patients have narrow chamber angle and can be diagnosed by goniscopy, which may cause discomfort and relies too much on personal experience. Anterior segment OCT is able to provide 3D scan of the anterior chamber and assist the ophthalmologists evaluate the condition of chamber angle. It’s faster and objective compare with goniscopy.

  • Computer Vision
  • Last Updated On: 
    Tue, 07/09/2019 - 09:31

    Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.

  • Computer Vision
  • Last Updated On: 
    Tue, 07/09/2019 - 08:57

    Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.

  • Computer Vision
  • Last Updated On: 
    Mon, 07/08/2019 - 10:00

    For effectness verification of our proposed neural network, a total of 19,368 lab-made images of butterfly specimensspanning 48 sub-species areutilizedas testing samples, while 116,208 augmented images are employed for training.

  • Computer Vision
  • Last Updated On: 
    Mon, 07/01/2019 - 08:44

    This dataset contains laser scans of PCBs as explained in "Fault Diagnosis in Microelectronics Attachment via Deep Learning Analysis of 3D Laser Scans". On the left and right image, we have a closer look at one circuitmodule of a PCB , before and after die attachment. Notice the different types of glue annotated as A, B, C, D and E. On each circuit there are four glue deposits on each type where approximately the same quantity of glue has been placed. As explainedin our paper the top three deposits are used for training and the bottom one for testing.

  • Computer Vision
  • Last Updated On: 
    Thu, 06/20/2019 - 06:40

    test file

  • Computer Vision
  • Last Updated On: 
    Mon, 06/17/2019 - 06:49