6 citations · 9 across the 4 of their papers we have counts for
4 papers
Meta Approach to Data Augmentation Optimization
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe +1
Data augmentation policies drastically improve the performance of image recognition tasks, especially when the policies are optimized for the target data and tasks. In this paper,…
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe +1
Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown…
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Leonardo Rundo, Changhee Han, Yudai Nagano +12
Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, th…
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Leonardo Rundo, Changhee Han, Jin Zhang +10
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), wh…