Mr. Tomoki Uemura Profile

Tomoki Uemura

at Massachusetts General Hospital

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Publications (11)

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Unsupervised 3D prediction model for pulmonary disease progression in chest CT

Janne Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 12037, 120370O (2022) https://doi.org/10.1117/12.2612375

KEYWORDS: Computed tomography, 3D modeling, Chest, Data modeling, Compound parabolic concentrators, 3D image processing, Lung, Visualization, Statistical analysis, Network architectures

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Proceedings Article | 15 March 2021 Poster + Presentation + Paper

U-radiomics for predicting survival of patients with COVID-19

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida

Proceedings Volume 11601, 1160110 (2021) https://doi.org/10.1117/12.2581907

KEYWORDS: Lung, Computed tomography, Tissues, Proteins, Chest

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Proceedings Article | 12 March 2021 Poster + Presentation + Paper

Unsupervised survival prediction model from CT images of patients with COVID-19

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida

Proceedings Volume 11597, 1159732 (2021) https://doi.org/10.1117/12.2581929

KEYWORDS: Computed tomography, Chest, Network architectures, Statistical modeling, Proteins, Performance modeling, Lung, Computer programming

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Proceedings Article | 15 February 2021 Presentation + Paper

3D deep learning for computer-aided detection of serrated polyps in CT colonography

Janne Näppi, Tomoki Uemura, Perry Pickhardt, David Kim, Hiroyuki Yoshida

Proceedings Volume 11597, 115970I (2021) https://doi.org/10.1117/12.2581776

KEYWORDS: Virtual colonoscopy, Computer-aided diagnosis, Colorectal cancer, Error control coding, Computer aided diagnosis and therapy, Cancer, Coating, Image resolution, Computed tomography, 3D image processing

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Proceedings Article | 17 March 2020 Paper

GAN-based survival prediction model from CT images of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11318, 113181F (2020) https://doi.org/10.1117/12.2551369

KEYWORDS: Computed tomography, Chest, Pulmonary fibrosis, Performance modeling, Network architectures, Physiology, Lung, Biological research, Image analysis, 3D modeling

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We developed a novel survival prediction model for images, called pix2surv, based on a conditional generative adversarial network (cGAN), and evaluated its performance based on chest CT images of patients with idiopathic pulmonary fibrosis (IPF). The architecture of the pix2surv model has a time-generator network that consists of an encoding convolutional network, a fully connected prediction network, and a discriminator network. The fully connected prediction network is trained to generate survival-time images from the chest CT images of each patient. The discriminator network is a patchbased convolutional network that is trained to differentiate the “fake pair” of a chest CT image and a generated survivaltime image from the “true pair” of an input CT image and the observed survival-time image of a patient. For evaluation, we retrospectively collected 75 IPF patients with high-resolution chest CT and pulmonary function tests. The survival predictions of the pix2surv model on these patients were compared with those of an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by use of a two-sided t-test with bootstrapping. Concordance index (C-index) and relative absolute error (RAE) were used as measures of the prediction performance. Preliminary results showed that the survival prediction by the pix2surv model yielded more than 15% higher C-index value and more than 10% lower RAE values than those of the GAP index. The improvement in survival prediction by the pix2surv model was statistically significant (P < 0.0001). Also, the separation between the survival curves for the low- and high-risk groups was larger with pix2surv than that of the GAP index. These results show that the pix2surv model outperforms the GAP index in the prediction of the survival time and risk stratification of patients with IPF, indicating that the pix2surv model can be an effective predictor of the overall survival of patients with IPF.

Proceedings Article | 16 March 2020 Paper

U-radiomics for predicting survival of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113140T (2020) https://doi.org/10.1117/12.2551273

KEYWORDS: Computed tomography, Lung, Pulmonary fibrosis, Chest, Performance modeling

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Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography

Janne Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113143C (2020) https://doi.org/10.1117/12.2549793

KEYWORDS: Spectral computed tomography, Machine learning, Computed tomography, Cancer, Computer aided diagnosis and therapy, Virtual colonoscopy, Colorectal cancer, 3D image processing, Radiology

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Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography

Tomoki Uemura, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 1131435 (2020) https://doi.org/10.1117/12.2549103

KEYWORDS: 3D modeling, Computer aided diagnosis and therapy, 3D image processing, Medical imaging, Performance modeling, Computed tomography, Virtual colonoscopy, Visualization, Data modeling, Solid modeling

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Proceedings Article | 16 March 2020 Paper

Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis

Mina Rezaei, Tomoki Uemura, Janne Näppi, Hiroyuki Yoshida, Christoph Lippert, Christoph Meinel

Proceedings Volume 11314, 113140E (2020) https://doi.org/10.1117/12.2551166

KEYWORDS: Medical imaging, Image classification

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Proceedings Article | 27 March 2019 Paper

Deep radiomic precision CT imaging for prognostic biomarkers for interstitial lung diseases

Tomoki Uemura, Mikio Matsuhiro, Chinatsu Watari, Janne Näppi, Radin Nasirudin, Toru Hironaka, Yoshiki Kawata, Noboru Niki, Hiroyuki Yoshida

Proceedings Volume 10954, 109541E (2019) https://doi.org/10.1117/12.2512058

KEYWORDS: Lung, Computed tomography, Image analysis, Performance modeling, Convolutional neural networks, Pulmonary function tests, Statistical analysis, Physiology, Gallium nitride, Feature extraction

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Proceedings Article | 18 March 2019 Presentation + Paper

Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography

Tomoki Uemura, Janne Näppi, Huimin Lu, Hyoungseop Kim, Rie Tachibana, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10950, 1095013 (2019) https://doi.org/10.1117/12.2512173

KEYWORDS: Performance modeling, Virtual colonoscopy, Data modeling, Statistical modeling, Gallium nitride, Computed tomography, Image processing, Convolution, 3D modeling, Computer aided diagnosis and therapy

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Showing 5 of 11 publications

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