Search Results for author: Ryuichiro Hataya

Found 13 papers, 2 papers with code

Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels

no code implementations29 Sep 2021 Ryuichiro Hataya, Hideki Nakayama

Optimizing hyperparameters of machine learning algorithms especially for limited labeled data is important but difficult, because then obtaining enough validation data is practically impossible.

Hyperparameter Optimization Model Selection

Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval

no code implementations23 Mar 2021 Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, Ryuji Hamamoto

To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful.

Content-Based Image Retrieval Quantization

Graph Energy-based Model for Substructure Preserving Molecular Design

no code implementations9 Feb 2021 Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe

It is common practice for chemists to search chemical databases based on substructures of compounds for finding molecules with desired properties.

DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness

no code implementations1 Jan 2021 Ryuichiro Hataya, Hideki Nakayama

Convolutional Neural Networks (CNNs) are vulnerable to unseen noise on input images at the test time, and thus improving the robustness is crucial.

Data Augmentation Semantic Segmentation

Meta Approach to Data Augmentation Optimization

no code implementations14 Jun 2020 Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama

Data augmentation policies drastically improve the performance of image recognition tasks, especially when the policies are optimized for the target data and tasks.

Classification Data Augmentation +2

Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection

1 code implementation26 May 2020 Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Amina Bolatkan, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Jun Itami, Tatsuya Harada, Ryuji Hamamoto

In addition, we devise a metric to evaluate the anatomical fidelity of the reconstructed images and confirm that the overall detection performance is improved when the image reconstruction network achieves a higher score.

Anomaly Detection Image Reconstruction

Faster AutoAugment: Learning Augmentation Strategies using Backpropagation

1 code implementation ECCV 2020 Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama

In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods.

Investigating CNNs' Learning Representation under label noise

no code implementations ICLR 2019 Ryuichiro Hataya, Hideki Nakayama

Deep convolutional neural networks (CNNs) are known to be robust against label noise on extensive datasets.

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

no code implementations17 Apr 2019 Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga

The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.

Unifying semi-supervised and robust learning by mixup

no code implementations ICLR Workshop LLD 2019 Ryuichiro Hataya, Hideki Nakayama

In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.

Learning with noisy labels

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