Hidetoshi Matsuo
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View article: Deep Learning-Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models
Deep Learning-Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models Open
With the increasing use of postmortem imaging, deep learning (DL)-based automated analysis may assist in the detection of intracranial hemorrhages. However, limited postmortem data complicate model training. This study aims to assess the a…
View article: Deep Learning–Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models
Deep Learning–Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models Open
With the increasing use of postmortem imaging, deep learning (DL)-based automated analysis may assist in detecting intracranial hemorrhages. However, limited postmortem data complicate model training. This study aims to assess the accuracy…
View article: RadVLM: A Multitask Conversational Vision-Language Model for Radiology
RadVLM: A Multitask Conversational Vision-Language Model for Radiology Open
The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing vision-language models (VLMs) show promise in specific task…
View article: Exploring Multilingual Large Language Models for Enhanced TNM Classification of Radiology Report in Lung Cancer Staging
Exploring Multilingual Large Language Models for Enhanced TNM Classification of Radiology Report in Lung Cancer Staging Open
Background/Objectives: This study aimed to investigate the accuracy of Tumor, Node, Metastasis (TNM) classification based on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of multilingual large language models (LLMs) in both…
View article: [Applications] 11. Automatic Generation of Radiology Reports for Chest X-ray Images
[Applications] 11. Automatic Generation of Radiology Reports for Chest X-ray Images Open
View article: Exploring Multilingual Large Language Models for Enhanced TNM classification of Radiology Report in lung cancer staging
Exploring Multilingual Large Language Models for Enhanced TNM classification of Radiology Report in lung cancer staging Open
Background: Structured radiology reports remains underdeveloped due to labor-intensive structuring and narrative-style reporting. Deep learning, particularly large language models (LLMs) like GPT-3.5, offers promise in automating the struc…
View article: Development and web deployment of prediction model for pulmonary arterial pressure in chronic thromboembolic pulmonary hypertension using machine learning
Development and web deployment of prediction model for pulmonary arterial pressure in chronic thromboembolic pulmonary hypertension using machine learning Open
Background and purpose Mean pulmonary artery pressure (mPAP) is a key index for chronic thromboembolic pulmonary hypertension (CTEPH). Using machine learning, we attempted to construct an accurate prediction model for mPAP in patients with…
View article: Author Correction: Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers
Author Correction: Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers Open
View article: Fully automatic summarization of radiology reports using natural language processing with large language models
Fully automatic summarization of radiology reports using natural language processing with large language models Open
Purpose: Natural language processing using language models has yielded promising results in various fields. Language models can help improve the workflow of radiologists. This retrospective study aimed to construct and evaluate language mo…
View article: Fully automatic summarization of radiology reports using natural language processing with language models
Fully automatic summarization of radiology reports using natural language processing with language models Open
Natural language processing using language models has yielded promising results in various fields. The use of language models may help improve the workflow of radiologists. This retrospective study aimed to construct and evaluate language …
View article: Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers
Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers Open
“Preprocessing” is the first step required in brain image analysis that improves the overall quality and reliability of the results. However, it is computationally demanding and time-consuming, particularly to handle and parcellate complic…
View article: Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system
Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system Open
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for th…
View article: Comparison between pystan and numpyro in Bayesian item response theory: evaluation of agreement of estimated latent parameters and sampling performance
Comparison between pystan and numpyro in Bayesian item response theory: evaluation of agreement of estimated latent parameters and sampling performance Open
Purpose The purpose of this study is to compare two libraries dedicated to the Markov chain Monte Carlo method: pystan and numpyro. In the comparison, we mainly focused on the agreement of estimated latent parameters and the performance of…
View article: Development of pericardial fat count images using a combination of three different deep-learning models
Development of pericardial fat count images using a combination of three different deep-learning models Open
Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to …
View article: Usefulness of pystan and numpyro in Bayesian item response theory
Usefulness of pystan and numpyro in Bayesian item response theory Open
Purpose: The purpose of this study is to compare two libraries dedicated to Markov chain Monte Carlo method: pystan and numpyro. Materials and methods: Bayesian item response theory (IRT), 1PL-IRT and 2PL-IRT, were implemented with pystan …
View article: Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer
Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer Open
We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution …
View article: Bayesian multidimensional nominal response model for observer study of radiologists
Bayesian multidimensional nominal response model for observer study of radiologists Open
View article: Bayesian Multidimensional Nominal Response Model for Observer Study of Radiologists
Bayesian Multidimensional Nominal Response Model for Observer Study of Radiologists Open
Purpose This study proposes a Bayesian multidimensional nominal response model (MD-NRM) to statistically analyze the nominal response of multiclass classifications. Materials and methods First, for MD-NRM, we extended the conventional nomi…
View article: Unsupervised-learning-based method for chest MRI–CT transformation using structure constrained unsupervised generative attention networks
Unsupervised-learning-based method for chest MRI–CT transformation using structure constrained unsupervised generative attention networks Open
View article: Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study
Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study Open
View article: Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network
Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network Open
Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial…
View article: Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis
Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis Open
Deep learning with 3D DCNN in combination with FDG-PET/CT imaging results as well as clinical features comprise a novel potential tool shows flexibility for differential diagnosis of MPM.
View article: Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI
Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI Open
We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amount of imbalanced data when anomaly detection (AD) was combined with deep leaning (DL) mo…