  Infectious keratitis is the most common entities of corneal diseases, in
which pathogen grows in the cornea leading to inflammation and destruction of
the corneal tissues. Infectious keratitis is a medical emergency, for which a
rapid and accurate diagnosis is needed for speedy initiation of prompt and
precise treatment to halt the disease progress and to limit the extent of
corneal damage; otherwise it may develop sight-threatening and even
eye-globe-threatening condition. In this paper, we propose a sequential-level
deep learning model to effectively discriminate the distinction and subtlety of
infectious corneal disease via the classification of clinical images. In this
approach, we devise an appropriate mechanism to preserve the spatial structures
of clinical images and disentangle the informative features for clinical image
classification of infectious keratitis. In competition with 421
ophthalmologists, the performance of the proposed sequential-level deep model
achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic
accuracy achieved by ophthalmologists over 120 test images.
