137 citations · 366 across the 7 of their papers we have counts for
7 papers
Development of pericardial fat count images using a combination of three different deep-learning models
Takaaki Matsunaga, Atsushi Kono, Hidetoshi Matsuo +9
Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the…
Boosting Radiology Report Generation by Infusing Comparison Prior
Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian +5
Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often la…
Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks
Hidetoshi Matsuo, Mizuho Nishio, Munenobu Nogami +6
The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner facilitates the simultaneous acquisition of metabolic information via PET and morphological…
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
Mizuho Nishio, Shunjiro Noguchi, Hidetoshi Matsuo +1
Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on c…
Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model
Mizuho Nishio, Sho Koyasu, Shunjiro Noguchi +5
Background and Purpose: We aimed to develop and evaluate an automatic acute ischemic stroke-related (AIS) detection system involving a two-stage deep learning model. Methods: We in…
Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning
Mizuho Nishio, Koji Fujimoto, Kaori Togashi
Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal f…
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
Mizuho Nishio, Mitsuo Nishizawa, Osamu Sugiyama +4
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of…