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

, , , , и . CoRR, (2020)

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Ryo Sugihara

Ryo Kohsaka

Jong Won Ryoo

Mun Il Ryoo

So Young Ryou

 

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Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data., , , , и . HEALTHINF, стр. 93-104. SciTePress, (2016)Occurrence Probability of SNI in Accessing Service for Improving Accuracy of Service Identification., , , и . ICCE-Taiwan, стр. 817-818. IEEE, (2023)Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering., , , , и . KDD, стр. 1979-1988. ACM, (2015)A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification., , , , и . CoRR, (2020)Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization., , , , , и . KDD, стр. 783-792. ACM, (2018)Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements., , , , и . ICDM, стр. 1121-1126. IEEE Computer Society, (2013)Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes., , , , , , и . KDD, стр. 2041-2050. ACM, (2017)Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors., , , , и . CoRR, (2016)PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma., , , , , и . KDD, стр. 3826-3834. ACM, (2021)Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression., , , , , , и . KDD, стр. 2278-2286. ACM, (2019)