Multi-view learning over retinal thickness and visual sensitivity on glaucomatous eyes

Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

5 被引用数 (Scopus)

抄録

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 retinalthickness 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 Non-negative 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. These methods are kinds of multi-view learning methods because they utilize visual-field and retinal thickness data simultaneously. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF worked better for relatively small data size while CNNs 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.

本文言語英語
ホスト出版物のタイトルKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
出版社Association for Computing Machinery
ページ2041-2050
ページ数10
ISBN(電子版)9781450348874
DOI
出版ステータス出版済み - 8月 13 2017
外部発表はい
イベント23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, カナダ
継続期間: 8月 13 20178月 17 2017

出版物シリーズ

名前Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Part F129685

会議

会議23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
国/地域カナダ
CityHalifax
Period8/13/178/17/17

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム

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