Researcher Database

（Last updated : 2022-05-19 10:40:03）

HIZUKURI AKIYOSHI

Department / Course College of Science and Engineering Department of Electronic and Computer Engineering

Title / Position Assistant Professor

Other Affiliations
1.	Graduate School of Science and Engineering

Academic background
1.	2014/03 Degree Acquisition	│ Mie University Doctor of Engineering
2.	～2009/03	Department of Electrical and Electronic Engineering │ Faculty of Engineering │ Mie University │ Graduated
3.	～2011/03	Doctoral Program (1st Semester) │ Division of Electrical and Electronic Engineering │ Graduate School of Engineering │ Mie University │ Completed
4.	～2014/03	Doctoral Program (2nd Semester) │ Division of Systems Engineering │ Graduate School of Engineering │ Mie University │ Completed

Business career
1.	2018/04/01	│ Department of Electronic and Computer Engineering │ Ritsumeikan University
2.	2014/04/01 ～ 2018/03/31	│ │ Mizuho Information & Research Institute,INC.

Committee and society
1.	2019/04/01 ～	ICIPRob (International Conference on Image Processing and Robotics) International Program Committee

Research activities
1.		The Institute of Electronics, Information and Communication Engineers
2.		The Institute of Electrical Engineers of Japan
3.		The Japanese Society of Medical Imaging Technology
4.		Japan Society of Medical Physics
5.		Technical Committee on Industrial Application of Image Processing

Subject of research
1.	Image super-resolution
2.	Lesion segmentation

Research summary
Development of Computer-aided Diagnosis System for Medical Images ① Computer-aided Detection of Clustered Microcalcification on Mammograms 　In a computer-aided diagnosis scheme for evaluating the likelihood of malignancy of clustered microcalcifications on mammograms, it is necessary to segment individual calcifications correctly. The purpose of this study is to develop a computerized segmentation method for individual calcifications with various sizes while maintaining their shapes in the CADx schemes using a filter bank and an artificial neural network. ② Computerized Determination Scheme for Histological Classification of Breast Mass on Ultrasonographic Images 　It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study is to develop a computerized determination scheme for histological classification of breast mass on ultrasonographic images by using deep learning.

Present specialized field
Perceptual information processing, Intelligent informatics, Soft computing, Life/Health/Medical informatics, Medical systems (Keyword：machine learning, deep learning, image processing, medical image analysis)

Papers
1.	2022/06	Computerized Classification Method for 1p/19q Codeletion in Low Grade Gliomas from Brain MRI Images Using Three Dimensional Radiomics Features（Accepted, 掲載予定2022年6月） │ Journal of the Institute of Image Electronics Engineers of Japan (IIEEJ) │ (Co-authored)
2.	2021/03	Segmentation of Teeth in Panoramic Dental X-ray Images Using U-Net with a Loss Function Weighted on the Tooth Edge │ Radiological Physics and Technology │ 14 (1),pp.64-69 (Co-authored)
3.	2020/04	Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network │ Journal of Digital Imaging │ 33 (2),pp.497-503 (Co-authored)
4.	2018/07	Computer-aided Diagnosis Scheme for Determining Histological Classification of Breast Masses on Ultrasonographic Images using Convolutional Neural Network │ Diagnostics (Basel) │ 25 (8),pp.1-9. (Co-authored)
5.	2017/04	Segmentation Method of Breast Masses on Ultrasonographic Images Using Level Set Method Based on Statistical Model │ Journal of Biomedical Science and Engineering │ 10,pp.149-162 (Co-authored)
Display All（10）

Academic conference presentation
1.	2020/05/22	Computerized classification scheme for distinguishing between benign and malignant masses by analyzing multiple MRI sequences with convolutional neural network (Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020))
2.	2020/02	Construction of virtual normal dose CT images from ultra-low dose CT images using dilated residual networks (Proc. of SPIE Medical Imaging)
3.	2019/09/03	Determination of hyper-parameters in convolutional neural network for medical image applications using Bayesian optimization method
4.	2019/09/03	Improvement of frame rate in cine MRI using deep learning
5.	2018/12	Optimization Method of Hyper-Parameters in Convolutional Neural Network for Medical Image Application (104th RSNA Scientific Assembly and Annual Meeting (RSNA2018))
Display All（29）

Winning science prize
1.	2018/07	Asia Pacific Society for Computing and Information Technology Outstanding Research Achievement and Contribution
2.	2012/10	Fourth International Workshop on Regional Innovation Studies - Biomedical Engineering - Outstanding Paper Award (Computer-aided Diagnosis Scheme for Determining Histological Classification of Breast Masses using Clinical Features and Texture Features)

E-Mail Address