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Toru Hironaka

Observed name: Toru Hironakaa
ORCID: 0000-0002-8780-5850
Observed ORCID: 0000-0002-8780-5850
OpenAlex ID: A5042232270
Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography
2016 · Janne J. Näppi, Toru Hironaka, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 35
Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography
2020 · Tomoki Uemura, Janne J. Näppi, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 32
Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography
2018 · Rie Tachibana, Janne J. Näppi, et al. · Radiographics · 26
Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning
2023 · Yukihiro Iida, Janne Näppi, et al. · Oral Radiology · 21
Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT
2021 · Tomoki Uemura, Janne J. Näppi, et al. · Medical Image Analysis · 20
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Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography
2017 · Janne J. Näppi, Perry Pickhardt, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 11
U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19
2021 · Janne J. Näppi, Tomoki Uemura, et al. · Scientific Reports · 9
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Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography
2017 · Kensuke Umehara, Janne J. Näppi, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 9
Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study
2022 · Rie Tachibana, Janne J. Näppi, et al. · Cancers · 8
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Detection of colorectal masses in CT colonography: application of deep residual networks for differentiating masses from normal colon anatomy
2018 · Hiroyuki Yoshida, Janne J. Näppi, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 7
Deep learning for electronic cleansing in dual-energy CT colonography
2016 · Rie Tachibana, Janne J. Näppi, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 6
Electronic cleansing in CT colonography using a generative adversarial network
2019 · Rie Tachibana, Janne Näppi, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 5
GAN-based survival prediction model from CT images of patients with idiopathic pulmonary fibrosis
2020 · Tomoki Uemuraa, Chinatsu Watari, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 4
Automated detection of colorectal polyps in photon-counting CT colonography
2023 · Janne J. Näppi, Toru Hironaka, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 3
Self-supervised generative adversarial network for electronic cleansing in dual-energy CT colonography
2020 · Rie Tachibana, Janne J. Näppi, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 3
Deep radiomic precision CT imaging for prognostic biomarkers for interstitial lung diseases
2019 · Tomoki Uemura, Mikio Matsuhiro, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 3
Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography
2019 · Tomoki Uemura, Janne J. Näppi, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 3
Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease
2018 · Hiroyuki Yoshida, Chinatsu Watari, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 3
Performance evaluation of multi-material electronic cleansing for ultra-low-dose dual-energy CT colonography
2016 · Rie Tachibana, Naja Kohlhase, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 3
Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis
2019 · José Raniery Ferreira, Marcel Koenigkam Santos, et al. · International Journal of Computer Assisted Radiology and Surgery · 2
3D generative AI for electronic cleansing in CT colonography
2024 · Rie Tachibana, Janne J. Näppi, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 1
Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography
2024 · Janne J. Näppi, Toru Hironaka, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 1
Electronic cleansing by unpaired contrastive learning in non-cathartic laxative-free CT colonography
2022 · Janne J. Näppi, Rie Tachibana, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 1
Unsupervised 3D prediction model for pulmonary disease progression in chest CT
2022 · Janne J. Näppi, Tomoki Uemura, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 1
Comparative performance of self-supervised 3D-ResNet-GAN for electronic cleansing in single- and dual-energy CT colonography
2021 · Rie Tachibana, Janne J. Näppi, et al. · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · 1
Works count: 37
Citation count: 220
H-index: 8
I10-index: 6
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