Retina layers thickness guided vision transformer for glaucoma diagnosis

Vision transformers generate both high accuracy and pathology related attention maps for clinical decision. We evaluated the performance of DeiT for glaucoma diagnosis with different training methods and inputs. Retia layers thickness maps and enface images were compared for pathology pattern, while transfer learning vs. re-training was tested for attention maps patterns. For example, the pathological change of glaucoma is regional RNFL thinning and thus the retraining with thickness maps yielded superior diagnostic accuracy. Other vascular disease might benefit from transfer learning with enface images. DeiT outperformed other benchmark models with the distilled superior small objects detection.