Mizuho Nishio

发表

Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network

Mizuho Nishio, Chisako Muramatsu, Shunjiro Noguchi, 2020, Comput. Biol. Medicine.

Bayesian Statistical Model of Item Response Theory in Observer Studies of Radiologists.

Mizuho Nishio, Thai Akasaka, Ryo Sakamoto, 2019, Academic radiology.

Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques

Mizuho Nishio, Shunjiro Noguchi, Kaori Togashi, 2020, Comput. Biol. Medicine.

Abdominal CT with Single-Energy Metal Artifact Reduction (SEMAR): Initial Experiences

Mizuho Nishio, Takeshi Yoshikawa, Naoki Sugihara, 2014 .

A case study regarding clinical performance evaluation method of medical device software for approval

Tomohiro Kuroda, Tatsuya Ito, Takeshi Kubo, 2019, Medical Imaging.

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, 2020, Scientific Reports.

Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net

Koji Fujimoto, Mizuho Nishio, Shunjiro Noguchi, 2020, Applied Sciences.

Tumor segmentation on FDG-PET: usefulness of locally connected conditional random fields

Mizuho Nishio, Hisanobu Koyama, Kazuro Sugimura, 2015, Medical Imaging.

Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and experts’ knowledge

Koji Fujimoto, Takeshi Kubo, Hiroyuki Yamamoto, 2018, PloS one.

Adaptive Voxel Matching for Temporal CT Subtraction

Takeshi Kubo, Mizuho Nishio, Kaori Togashi, 2020, Journal of Digital Imaging.

Improving breast mass classification by shared data with domain transformation using a generative adversarial network

Chisako Muramatsu, Takeshi Kubo, Mizuho Nishio, 2020, Comput. Biol. Medicine.

Lung segmentation on chest X‐ray images in patients with severe abnormal findings using deep learning pdf

Mizuho Nishio, Kaori Togashi, Koji Fujimoto, 2019, Int. J. Imaging Syst. Technol..

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

Tomohiro Kuroda, Ryosuke Kojima, Mizuho Nishio, 2017, PloS one.

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Mizuho Nishio, Tatsuo Yamashita, Kaori Sasaki, 2017, Heliyon.

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Tomohiro Kuroda, Takeshi Kubo, Mizuho Nishio, 2018, PloS one.

Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model

Mizuho Nishio, Thai Akasaka, Sho Koyasu, 2020, Comput. Methods Programs Biomed..

Automatic segmentation of the uterus on MRI using a convolutional neural network

Mizuho Nishio, Koji Fujimoto, Kaori Togashi, 2019, Comput. Biol. Medicine.

Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis

Takamichi Murakami, Masatoshi Hori, Mizuho Nishio, 2020, Applied Sciences.

Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction.

Mizuho Nishio, Hirotsugu Nakai, Koji Fujimoto, 2020, Academic radiology.

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks pdf

Florian Wiesinger, Takamichi Murakami, Mizuho Nishio, 2021, ArXiv.

Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network

Hiroshi Fujita, Chisako Muramatsu, Mizuho Nishio, 2021, Frontiers in Artificial Intelligence.

Application of the homology method for quantification of low-attenuation lung region inpatients with and without COPD

Kazuaki Nakane, Mizuho Nishio, Yutaka Tanaka, 2016, International journal of chronic obstructive pulmonary disease.

Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images

Takeshi Kubo, Mizuho Nishio, Kaori Togashi, 2018, European Radiology.

Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing.

Takeshi Yoshikawa, Mizuho Nishio, Sumiaki Matsumoto, 2012, AJR. American journal of roentgenology.

Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity.

Mizuho Nishio, M. Nishio, Chihiro Nagashima, 2017, Academic radiology.

Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection.

Takeshi Yoshikawa, Mizuho Nishio, Yoshiharu Ohno, 2016, AJR. American journal of roentgenology.

Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region

Takeshi Kubo, Kazuaki Nakane, Mizuho Nishio, 2017, PloS one.

Magnetic Resonance Imaging for Lung Cancer

Takeshi Yoshikawa, Mizuho Nishio, Sumiaki Matsumoto, 2013, Journal of thoracic imaging.

Emphysema quantification on low-dose CT using percentage of low-attenuation volume and size distribution of low-attenuation lung regions: effects of adaptive iterative dose reduction using 3D processing.

Takeshi Yoshikawa, Mizuho Nishio, Sumiaki Matsumoto, 2014, European journal of radiology.

Heterogeneity in pulmonary emphysema: Analysis of CT attenuation using Gaussian mixture model

Mizuho Nishio, M. Nishio, Yutaka Tanaka, 2018, PloS one.