|Hiroki Sugiura||The University of Tokyo|
|Taichi Kiwaki||The University of Tokyo|
|Siamak Yousefi||University of Tennessee|
|Hiroshi Murata||The University of Tokyo|
|Ryo Asaoka||The University of Tokyo|
|Kenji Yamanishi||The University of Tokyo|
glaucoma is diagnosed on the basis of visual field sensitivity (VF), which is time-consuming, costly, and noisy. .the authors propose a new methodology for estimating VF from RT in glaucomatous eyes.The authors can thereby avoid overfitting of a CNN to small sized data.
Conventionally, glaucoma is diagnosed on the basis of visual field sensitivity (VF). However, the VF test is time-consuming, costly, and noisy. Using retinal thickness (RT) for glaucoma diagnosis is currently desirable. Thus, we propose a new methodology for estimating VF from RT in glaucomatous eyes. The key ideas are to use our new methods of pattern-based regularization (PBR) and pattern-based visualization (PBV) with convolutional neural networks (CNNs). PBR effectively conducts supervised learning of RT-VF relations in combination with unsupervised learning from non-paired VF data. We can thereby avoid overfitting of a CNN to small sized data. PBV visualizes functional correspondence between RT and VF with its nonlinearity preserved. We empirically demonstrate with real datasets that a CNN with PBR achieves the highest estimation accuracy to date and that a CNN with PBV is effective for knowledge discovery in an ophthalmological context.