Papers - Tanaka, Toshiyuki

Prediction of Microsatellite Instability in Colorectal Cancer Based on Deep Learning

Takuya Fujii, Shingo Sakashita, Genichiro Ishii, Toshiyuki Tanaka

The 2nd International Conferene on ICT Application Research (IAR2024)  2024.09

Research paper (international conference proceedings), Corresponding author, Accepted

Healing Process Evaluation for Severe Burns Using Image Processing

Tatsuro Yoshikawa, Soichiro Kato, Yoshihiro Yamaguchi, Toshiyuki Tanaka

the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2024)  2024.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Brain Image Analysis of ASD Using Large-Scale Deep Learning Models

Amane Okuno, Ryoichi Yokoyama, Takehiro Jimbo, Hidetoshi Matsuo, Makoto Nishimori, Toshikaze Chiba, Keiya Goto, Toshiyuki Tanaka

the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2024)  2024.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Diagnosis Support System for Cervical Cancer Using Image Processing

Taisei Tanaka, Shingo Sakashita, Genichiro Ishii, Toshiyuki Tanaka

the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2024)  2024.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Non-Contact Heart Rate Measurement Using 60 GHz FMCW Radar

Ryosuke Shimizu, Yutaka Tomita, Toshiyuki Tanaka

the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2024)  2024.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Evaluation of the Depth of Invasion of Esophageal Cancer Using Deep Learning

Toru Akaza, Shingo Sakashita, Genichiro Ishii, Toshiyuki Tanaka

the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2024)  2024.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Quantitative skin surface hydration measurement by visible optical image processing: A pilot study

Wu Y., Ran J., Tanaka T., Liu Q.

Skin Research and Technology (Skin Research and Technology)  30 ( 6 )  2024.06

ISSN  0909752X

　View Summary

Background: Skin barrier function is significantly impacted by skin moisture. Most non-invasive evaluation techniques to measure skin surface hydration relying on its electrical properties, which are limited in scope and have unstable operations. Applying image processing for skin hydration assessment is uncommon, with an emphasis on skin-capacitive pictures and near-infrared images in general, which demand a certain spectrum. As a result, there is an increasing need for wide-area skin hydration evaluation and mapping. Objective: The study aims to propose a quantitative evaluation algorithm for skin surface hydration from visible-light images. Materials and methods: Three devices were applied to measure skin hydration: skin image capture device and two recognized commercial skin devices. A digital image processing system creates a new index, called GVR, to symbolize skin surface moisture. The CLAHE algorithm was applied to enhance the contrast of skin image, and after calculating it with the monochrome image, the skin reflectance image was segmented. The GVR was estimated using the values of the individual sites and the entire skin. The correlation coefficient between the three methods was examined using statistical analysis to assess the performance of GVR. Results: Skin hydration estimated from visible-light images is influenced by the entire facial structure in addition to specific areas. The electrical and visible image evaluations showed a strong association with a significant difference. Conclusion: It was discovered that reflecting measures from visible images provide a quick and efficient way to quantify the moisture of the skin's surface.

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Diagnosis Support System for Cervical Cancer Using Image Processing

Taisei Tanaka, Toshiyuki Tanaka

The 1st International Conference on ICT Application Research ( the International ICT Application Research Society)   2023.09

Research paper (international conference proceedings), Joint Work, Accepted

Burn Area Extraction Method by Depth with Image Processing

Yuki Shimbo, Soichiro Kato, Yoshihiro Yamgaguchi, Toshiyuki Tanaka

The 1st International Conference on ICT Application Research ( the International ICT Application Research Society)   2023.09

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Non-contact heart rate measurement of multiple targets using millimeter-wave radar

Ryosuke Shimizu, Toshiyuki Tanaka

The 1st International Conference on ICT Application Research ( the International ICT Application Research Society)   2023.09

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Automation of Cancer Area Segmentation in HE Stained Images for Area Calculation

Sota Morita and Shingo Sakashita and Genichiro Ishii, Toshiyuki Tanaka

SICE Annual Conference 2023 (SICE)   2023.09

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Prediction of Microsatellite Instability in Colorectal Cancer Based on Deep Learning

Takuya Fujii, Shingo Sakashita, Genichiro Ishii, Toshiyuki Tanaka

45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 (IEEE)   2023.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Evaluation Method for Healing Process in Severe Burns Using Image Processing

Tatsuro Yoshikawa, Soichiro Kato, Yoshihiro Yamaguchi, Toshiyuki Tanaka

45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 (IEEE)   2023.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Heart rate measurement using millimeter-wave radar

Yuma Semba, Yutaka Tomita, Toshiyuki Tanaka

45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 (IEEE)   2023.07

Research paper (international conference proceedings), Corresponding author, Accepted

Virtual Immunostaining of Esophageal Cancer from HE Stained Images using Deep Learning

Yuri Nakamura and Shingo Sakashita and Genichiro Ishii, Toshiyuki Tanaka

45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 (IEEE)   2023.07

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted

Tumor-identification method for predicting recurrence of early-stage lung adenocarcinoma using digital pathology images by machine learning

Hattori H., Sakashita S., Tsuboi M., Ishii G., Tanaka T.

Journal of Pathology Informatics (Journal of Pathology Informatics)  14 2023.01

ISSN  22295089

　View Summary

Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.

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Virtual Immunostaining From HE-Staining Images By Image Processing

Tanaka, Toshiyuki, Takahashi, Ryosuke; Sakashita, Shingo, Ishii, Genichiro

(The IUPESM World Congress on Medical Physics and Biomedical Engineering 2022)  2022   150 - 150 2022.06

Research paper (international conference proceedings), Joint Work, Lead author, Accepted

Cancer Segmentation Methods of H&E Stain Images

Morita, Sota, Sakashita, Shingo, Ishii, Genichiro, Tanaka, Toshiyuki

The IUPESM World Congress on Medical Physics and Biomedical Engineering 2022 2022   190 - 190 2022.06

Research paper (international conference proceedings), Joint Work, Last author, Accepted

Virtual Immunostaining From HE-Staining Images By Image Processing

Tanaka, Toshiyuki, Takahashi, Ryosuke; Sakashita, Shingo, Ishii, Genichiro

The IUPESM World Congress on Medical Physics and Biomedical Engineering 2022 (IEEE)   2022.06

Research paper (international conference proceedings), Joint Work, Lead author, Accepted

Cancer Segmentation Methods of H&E Stain Images

Morita, Sota, Sakashita, Shingo, Ishii, Genichiro, Tanaka, Toshiyuki

Proceedings of IFMBE (IFMBE)   2022.06

Research paper (international conference proceedings), Joint Work, Corresponding author, Accepted