29 citations · 123 across the 37 of their papers we have counts for
11 papers · 1 filter
Empirical Study of Easy and Hard Examples in CNN Training
Ikki Kishida, Hideki Nakayama
Deep Neural Networks (DNNs) generalize well despite their massive size and capability of memorizing all examples. There is a hypothesis that DNNs start learning from simple pattern…
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…
Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior
Raphael Shu, Jason Lee, Hideki Nakayama +1
Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latenc…
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis
Changhee Han, Leonardo Rundo, Kohei Murao +5
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a…
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection
Changhee Han, Yoshiro Kitamura, Akira Kudo +6
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging,…
Learning More with Less: GAN-based Medical Image Augmentation
Changhee Han, Kohei Murao, Shin'ichi Satoh +1
Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and an…
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +5
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fra…
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…
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +4
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transf…
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
Changhee Han, Kohei Murao, Tomoyuki Noguchi +5
Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data…