KDD Papers

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes

Toshimitsu Uesaka (The University of Tokyo);Kai Morino (The University of Tokyo);Hiroki Sugiura (The University of Tokyo);Taichi Kiwaki (The University of Tokyo);Hiroshi Murata (The University of Tokyo);Ryo Asaoka (The University of Tokyo);Kenji Yamanishi (The University of Tokyo)


Dense measurements of visual-field, which is necessary to detect glaucoma, is known as very costly and labor intensive. Recently, measurement of retinal-thickness can be less costly than measurement of visual-field. Thus, it is sincerely desired that the retinal-thickness could be transformed into visual-sensitivity data somehow. In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography(OCT). The first method called Affine-Structured Nonnegative Matrix Factorization(ASNMF) which is able to cope with both the estimation of visual-field and the discovery of deep glaucoma knowledge. While, the second is based on Convolutional Neural Networks (CNNs) which demonstrates very high estimation performance. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF method worked better for relatively small data size while CNN-based one did for relatively large data size. In addition, some clinical knowledge are discovered via ASNMF. To the best of our knowledge, this is the first paper to address the dense estimation of the visual-field based on the retinal-thickness data.