Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
This addresses the need for rapid and accurate diagnosis of infectious keratitis, a medical emergency, though it is incremental as it applies a novel deep learning method to a specific clinical domain.
The paper tackled the problem of diagnosing infectious keratitis from clinical images by proposing a sequential-level deep learning model, which achieved 80.00% diagnostic accuracy, significantly outperforming 421 ophthalmologists who scored 49.27% on 120 test images.
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.