Hidetoshi Matsuo

Specially Appointed Assistant Professor

Kobe University

Japan

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Hidetoshi Matsuo is a Specially Appointed Assistant Professor at Kobe University, Japan. His research areas include deep learning applications in medical imaging, particularly in detecting intracranial hemorrhages, cancer grading, and predicting patient prognosis using advanced computational models. He has authored several recent articles focusing on natural language processing in radiology, automated classification of pneumonia, and generative models for medical image transformation.

Articles (12)

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2025

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2024

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2023

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2023

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2023

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2022

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2022

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2021

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2021

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2021

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2020

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2020

Collaborators (1)

Koji Fujimoto

特定准教授 / Program-Specific Associate Professor

Kyoto University

JAPAN

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Hidetoshi Matsuo

Research Interests

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Articles (12)

Deep Learning-Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models

Fully automatic summarization of radiology reports using natural language processing with large language models

Bayesian multidimensional nominal response model for observer study of radiologists

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data

Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study

Unsupervised-learning-based method for chest MRI–CT transformation using structure constrained unsupervised generative attention networks

Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis

Deep learning model for predicting gestational age after the first trimester using fetal MRI

Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network

Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI.

Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods.

Collaborators (1)

Koji Fujimoto

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