Deep learning for the preoperative diagnosis of metastatic cervical lymph nodes on contrast-enhanced computed tomography in patients with oral squamous cell carcinoma H Tomita, T Yamashiro, J Heianna, T Nakasone, T Kobayashi, S Mishiro, ... Cancers 13 (4), 600, 2021 | 32 | 2021 |
Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients … M Nakajo, M Jinguji, A Tani, H Kikuno, D Hirahara, S Togami, ... Molecular Imaging and Biology 23, 756-765, 2021 | 28 | 2021 |
Effect of the pixel interpolation method for downsampling medical images on deep learning accuracy D Hirahara, E Takaya, M Kadowaki, Y Kobayashi, T Ueda Journal of Computer and Communications 9 (11), 150-156, 2021 | 19 | 2021 |
Effects of data count and image scaling on deep learning training D Hirahara, E Takaya, T Takahara, T Ueda PeerJ Computer Science 6, e312, 2020 | 19 | 2020 |
Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients M Nakajo, M Jinguji, A Tani, E Yano, CK Hoo, D Hirahara, S Togami, ... Abdominal Radiology, 1-10, 2022 | 18 | 2022 |
Preliminary assessment for the development of CADe system for brain tumor in MRI images utilizing transfer learning in Xception model D Hirahara 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 922-924, 2019 | 14 | 2019 |
The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with … M Nakajo, H Nagano, M Jinguji, Y Kamimura, K Masuda, K Takumi, ... The British Journal of Radiology 96 (1149), 20220772, 2023 | 12 | 2023 |
Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a … H Tomita, T Kobayashi, E Takaya, S Mishiro, D Hirahara, A Fujikawa, ... European Radiology 32 (8), 5353-5361, 2022 | 9 | 2022 |
Application of a machine learning approach to characterization of liver function using 99mTc-GSA SPECT/CT M Nakajo, M Jinguji, A Tani, D Hirahara, H Nagano, K Takumi, T Yoshiura Abdominal Radiology 46, 3184-3192, 2021 | 8 | 2021 |
Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer K Kawaji, M Nakajo, Y Shinden, M Jinguji, A Tani, D Hirahara, I Kitazono, ... Molecular Imaging and Biology 25 (5), 923-934, 2023 | 3 | 2023 |
Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac … M Nakajo, D Hirahara, M Jinguji, S Ojima, M Hirahara, A Tani, K Takumi, ... Japanese Journal of Radiology, 1-9, 2024 | 2 | 2024 |
Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer T Haraguchi, Y Kobayashi, D Hirahara, T Kobayashi, E Takaya, MT Nagai, ... Journal of X-Ray Science and Technology 31 (3), 627-640, 2023 | 2 | 2023 |
Fundamental study on preliminary image processing at time development of CNN using chest radiography D Hirahara, E Yuda, T Takahara, Y Kobayashi 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech …, 2019 | 2 | 2019 |
All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing C Han, K Shibano, W Ozaki, K Osaki, T Haraguchi, D Hirahara, S Kimura, ... Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and …, 2024 | 1 | 2024 |
Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma., 2021 … H Tomita, T Yamashiro, J Heianna, T Nakasone, T Kobayashi, S Mishiro, ... DOI: https://doi. org/10.3390/cancers13040600, 2021 | 1 | 2021 |
Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases M Nakajo, D Hirahara, M Jinguji, M Hirahara, A Tani, H Nagano, K Takumi, ... Japanese Journal of Radiology, 1-10, 2024 | | 2024 |
Machine Learning Analysis of Predictors for Inhaled Nitric Oxide Therapy Administration Time Post Congenital Heart Disease Surgery: A Single-Center Observational Study S Niiyama, T Nakashima, K Ueno, D Hirahara, M Nakajo, Y Madokoro, ... Cureus 16 (7), 2024 | | 2024 |
The usefulness of deep learning-based ensemble learning method using 18F-FDG-PET/CT radiomic features for differentiating between benign and malignant parotid gland diseases M Nakajo, D Hirahara, M Jinguji, M Hirahara, A Tani, T Yoshiura Journal of Nuclear Medicine 65 (supplement 2), 241080-241080, 2024 | | 2024 |
PD19-09 ARE THERE DIFFERENCES IN MRI FINDINGS BETWEEN CRIBRIFORM AND NON-CRIBRIFORM CANCER? AN ANALYSIS USING RADIOMICS AND DELTA-RADIOMICS K Enomoto, S Yoshida, H Izumi, S Uehara, Y Matsuoka, K Yamamoto, ... Journal of Urology 211 (5S), e443, 2024 | | 2024 |
MP30-08 DELTA-RADIOMICS ANALYSIS IN COMPARISON TO RADIOMICS ANALYSIS USING DYNAMIC COMPUTED TOMOGRAPHY FOR PREOPERATIVE RISK STRATIFICATION IN UPPER URINARY TRACT UROTHELIAL … M Fujiwara, D Hirahara, T Saho, E Takaya, S Matsumoto, K Yoshitomi, ... Journal of Urology 211 (5S), e493, 2024 | | 2024 |