Estimating Glaucomatous Visual Sensitivity From Retinal Thickness By Using Pattern-Based Regularizat

Authors:
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

Introduction:

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.

Abstract:

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.

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