Ryo Asaoka

发表

Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2015, KDD.

Combining Optical Coherence Topography measurements using the ‘Random Forest’ decision tree classifier improves the diagnosis of glaucoma

Koichiro Sugimoto, Makoto Aihara, Ryo Asaoka, 2013 .

A method to identify the edge of visual field scotoma; the estimation of prediction error of the visual field sensitivity is large at the steep ‘edge’ of scotoma

Hiroshi Murata, Ryo Asaoka, Chihiro Mayama, 2013 .

The usefulness of cluster-based trend analysis in assessing visual field progression in open-angle glaucoma

Kenji Kashiwagi, Hiroshi Murata, Atsuya Miki, 2017 .

Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization

Siamak Yousefi, Kenji Yamanishi, Hiroshi Murata, 2018, KDD.

Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements

Ryota Tomioka, Kenji Yamanishi, Hiroshi Murata, 2013, 2013 IEEE 13th International Conference on Data Mining.

Validating the Usefulness of the "Random Forests" Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography.

Hiroshi Murata, Ryo Asaoka, Makoto Araie, 2017, American journal of ophthalmology.

A novel method to predicting quality of visual life and identifying essential visual field locations

Hiroshi Murata, Makoto Aihara, Ryo Asaoka, 2013 .

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification pdf

Jun Huang, Kenji Yamanishi, Atsushi Nitanda, 2020, ArXiv.

Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2016, HEALTHINF.

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

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2017, KDD.

Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression

Kenji Yamanishi, Hiroshi Murata, Linchuan Xu, 2019, KDD.

Comparison of standard automated perimetry and short wavelength automated perimetryto evaluate the structure-function relationship in retinitis pigmentosa

Tatsuya Inoue, Ryo Asaoka, Hiroshi Murata, 2019 .

Visualizing the hill of vision in 3D using the free programming language ‘R’

Richard A. Russell, David P. Crabb, David F. Garway-Heath, 2011, Graefe's Archive for Clinical and Experimental Ophthalmology.

Construction of a deep learning algorithm to automatically diagnose glaucoma using a fundus photograph.

Hiroshi Murata, Ryo Asaoka, Masaki Tanito, 2018 .

Use of optical coherence tomography angiography to estimate central visual function

Makoto Aihara, Tatsuya Inoue, Ryo Asaoka, 2018 .

Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors pdf

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2016, ArXiv.

A novel system to detect the progression of glaucomatous visual field damage: aggregating the results of point-wise analysis with the binomial test

Hiroshi Murata, Makoto Aihara, Ryo Asaoka, 2013 .

A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Kenji Yamanishi, Taichi Kiwaki, Kazuhiko Mori, 2021, Ophthalmology science.

PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma

Kenji Yamanishi, Taichi Kiwaki, Ryo Asaoka, 2021, KDD.

How Many Visual Fields Are Required to Precisely Predict Future Test Results in Glaucoma Patients When Using Different Trend Analyses?

Ryo Asaoka, Chihiro Mayama, Yuri Fujino, 2015, Investigative ophthalmology & visual science.

The relationship between visual acuity and central visual field sensitivity in advanced glaucoma

Ryo Asaoka, R. Asaoka, 2013, British Journal of Ophthalmology.

Glaucoma-Induced Optic Disc Morphometric Changes and Glaucoma Diagnostic Ability of Heidelberg Retina Tomograph II in Highly Myopic Eyes

Kazunori Miyata, Ryo Asaoka, Atsuo Tomidokoro, 2014, PloS one.

Mapping Glaucoma Patients' 30-2 and 10-2 Visual Fields Reveals Clusters of Test Points Damaged in the 10-2 Grid That Are Not Sampled in the Sparse 30-2 Grid

Ryo Asaoka, R. Asaoka, 2014, PloS one.

Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

Ryo Asaoka, Makoto Araie, Hiroyo Hirasawa, 2014, PloS one.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms

Christopher Bowd, Linda M. Zangwill, Robert N. Weinreb, 2020, Translational vision science & technology.

Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

Makoto Aihara, Koichiro Sugimoto, Ryo Asaoka, 2013, BMJ Open.

The Heidelberg retina tomograph Glaucoma Probability Score: reproducibility and measurement of progression.

Nicholas G Strouthidis, Ryo Asaoka, Shaban Demirel, 2010, Ophthalmology.

Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

Yoshiaki Kiuchi, Ryo Asaoka, Yuri Fujino, 2019, Ophthalmology. Glaucoma.

Measuring Visual Field Progression in the Central 10 Degrees Using Additional Information from Central 24 Degrees Visual Fields and ‘Lasso Regression’

Ryo Asaoka, R. Asaoka, 2013, PloS one.

The association between photoreceptor layer thickness measured by optical coherence tomography and visual sensitivity in glaucomatous eyes

Kenji Inoue, Ryo Asaoka, Mieko Yanagisawa, 2017, PloS one.

A novel distribution of visual field test points to improve the correlation between structure-function measurements.

Richard A. Russell, Rizwan Malik, David P Crabb, 2012, Investigative ophthalmology & visual science.

Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma

Ryo Asaoka, Makoto Araie, Hiroyo Hirasawa, 2014, British Journal of Ophthalmology.

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets

Linda M. Zangwill, Kenji Kashiwagi, Katsuyoshi Suzuki, 2018, Investigative ophthalmology & visual science.

Mapping the Central 10° Visual Field to the Optic Nerve Head Using the Structure-Function Relationship.

Kenji Inoue, Ryo Asaoka, Mieko Yanagisawa, 2018, Investigative ophthalmology & visual science.

Identifying "preperimetric" glaucoma in standard automated perimetry visual fields.

Ryo Asaoka, Makoto Araie, Kazunori Hirasawa, 2014, Investigative ophthalmology & visual science.

Applying "Lasso" Regression to Predict Future Visual Field Progression in Glaucoma Patients.

Ryo Asaoka, Chihiro Mayama, Yuri Fujino, 2015, Investigative ophthalmology & visual science.

The association between structure-function relationships and cognitive impairment in elderly glaucoma patients

Ryo Asaoka, Megumi Honjo, Jiro Numaga, 2017, Scientific Reports.

The Relationship between Central Visual Field Damage and Motor Vehicle Collisions in Primary Open-Angle Glaucoma Patients

Ryo Asaoka, Kazuo Tsubota, K. Tsubota, 2014, PloS one.

Clustering visual field test points based on rates of progression to improve the prediction of future damage.

Ryo Asaoka, Hiroyo Hirasawa, Chihiro Mayama, 2014, Investigative ophthalmology & visual science.

A new approach to measure visual field progression in glaucoma patients using variational bayes linear regression.

Ryo Asaoka, Makoto Araie, Hiroshi Murata, 2014, Investigative ophthalmology & visual science.

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

Siamak Yousefi, Kenji Yamanishi, Taichi Kiwaki, 2018, American journal of ophthalmology.

Glaucomatous Visual Field Defect Severity and the Prevalence of Motor Vehicle Collisions in Japanese: A Hospital/Clinic-Based Cross-Sectional Study

Takayuki Abe, Ryo Asaoka, Kazuo Tsubota, 2015, Journal of ophthalmology.

Detection of Progression of Glaucomatous Visual Field Damage Using the Point-Wise Method with the Binomial Test

Ryo Asaoka, Hiroyo Hirasawa, Chihiro Mayama, 2013, PloS one.