Hidetaka Arimura Laboratory

Kyushu University, Division of Medical Quantum Sciences
Department of Health Sciences, Faculty of Medical Sciences
 
"Artificial intelligence-based diagnostic and treatment systems"
Our Keywords : Precision Medicine, Medical AI, Radiomics, Medical Images, Data Science, Medical Physics, Medical Image Analysis, Medical Image Processing, Pattern Recognition, Machine Learning, Radiation Physics
 
Introduction video of Arimura lab


 
AFOMP Monthly Webinar, Sept 2, 2021: Radiomics and Radiogenomics with AI for Oncology

2021



July/25-29/2021
Four presentations in AAPM2021 Virtual Meeting (Ninomiya, Urakami, Egashira, Kamezawa)

July/03/2021
Prof. Arimura had an Invited speech at 19th Asian Oceanian Society of Radiology AOCR2021, Malaysia

April/27/2021
Prof. Arimura had a speech at the 2021 Philippine Conference of Medical Physicists PCMP 2021 SMPRPPhilippine
"Predicting Futures from Medical Images -Radiomics and Machine Learning-"
 
April/18/2021
Mr. Ninomiya received President's Award Gold of The 121th Scientific Meeting of JSMP, Yokohama
"Radiogenomic imaging biopsy for EGFR-Mutated patients with non-small cell lung cancer based on contrast CT images using invariant Betti numbers"
 President’s Award of 121st Scientific Meeting of Japan Society of Medical Physics
 
April/18/2021
Mr. Hirose received Bronze Award of The 77th Annual Meeting of the JSRT
"Deep Learning-Based Prediction of Radiation Pneumonitis after Lung Cancer Stereotactic Body Radiation Therapy"
  The 77th Annual Meeting of the JSRT 
 
April/15~18/2021
JRC2021 PACIFICO Yokohama, and WEB
Four presentations in The 77 JSRT (Kodama, Egashira, Hirakawa, Hirose)
Three presentations in The 122 JSMP(Ninomiya,Huy, Arimura)
 
April/01/2021
New members have joined our laboratory!  Hamasaki, Uchino, Shirasaka, Fujii, Furuta, Please See Member
 
March/24/2021
Graduation Ceremony

January/27/2021
Mr. Ninomiya received Best Paper Award of International Forum on Medical Imaging in Asia IFMIA 2021 Hybrid conference , "Novel recognition approach of TKI-sensitizing EGFR mutations in non-small cell lung cancer patients using topologically invariant Betti numbers"
 
January/24-27/2021
Two presentations in The International Forum on Medical Imaging in Asia IFMIA 2021 Hybrid conference (Ninomiya, Cui)
  

 

 
 
Radiomics with artificial intelligence for precision medicine in radiation therapy
Hidetaka Arimura et. al.
Journal of Radiation Research, Vol. 60(1), pp. 150-157, January 2019
https://doi.org/10.1093/jrr/rry077
 


  

 

Robust identification of EGFR mutated NSCLC patients from three countries using Betti numbers
Kenta Ninomiya, et. al
PLOS ONE, Published: January 11, 2021 
https://doi.org/10.1371/journal.pone.0244354
 


 Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy
Taka-aki Hirose, et. al.
Scientific Reports Vol. 10, Article number: 20424 (2020), November 2020
DOI:https://doi.org/10.1038/s41598-020-77552-7
 


 
Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients
Quoc Le, et. al.
Scientific Reports 10, 21301 (2020), Published: December 2020
https://www.nature.com/articles/s41598-020-78338-7  


 

 
Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network
Leni Aziyus Fitria, et.al.
Physica Medica, Vol.78, pp.201-208, October 2020
https://doi.org/10.1016/j.ejmp.2020.09.007
 


 

 
 
 
Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks
Cui YunHao, et. al.
Journal of Radiation Research, Vol.62(2), March 2021, pp.346?355, Published: 22 January 2021
https://doi.org/10.1093/jrr/rraa132  


 

 

  
Automated Approach for Estimation of Grade Groups for Prostate Cancer based on Histological Image Feature Analysis,
Alamgir Hossain, et. al
The Prostate, Vol. 80(3), pp. 291-302, February 2020
 
DOI:10.1002/pros.23943 
 
 

 

 
Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy
Yudai Kai, Hidetaka Arimura,et.al.
Journal of Radiation Research,Vol. 61(2), pp.285?297, March 2020

https://doi.org/10.1093/jrr/rrz105 

  


  
Homological radiomics analysis for prognostic prediction in lung cancer patients.
Kenta NINOMIYA, et. al.
Physica Medica: European Journal of Medical Physics, Vol.69, pp.90-100, Januray 2020

DOI:10.1016/j.ejmp.2019.11.026 
 
 
 
 

Hidetaka Arimura, PhD

Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences,
Kyushu University
3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
Email : arimurah@med.kyushu-u.ac.jp

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