1 citations · 2 across the 3 of their papers we have counts for
3 papers
eess.IV2020
Multi-modal Datasets for Super-resolution
Haoran Li, Weihong Quan, Meijun Yan +3
Nowdays, most datasets used to train and evaluate super-resolution models are single-modal simulation datasets. However, due to the variety of image degradation types in the real w…
cs.CV2019★ 1 cited
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…
cs.CV2019★ 1 cited
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…