Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

Division display 141 - 160 of about 1,772 ／ All the affair displays >>

Octree cube constraints in PBD method for high resolution surgical simulation Reviewed

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

Proc.SPIE 2023.2

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-mixer Invited Reviewed

Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

Proc.SPIE 2023.2

A semantic segmentation method for laparoscopic images using semantically similar groups Reviewed

Leo Uramoto, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

Proc.SPIE 2023.2

Oesophagus Achalasia Diagnosis from Esophagoscopy Based on a Serial Multi-scale Network Invited

Kai Jiang, Masahiro Oda, Yuichiro Hayashi, Hironari Shiwaku, Masashi Misawa, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Vol. 11 ( 4 ) page： 1 - 10 2023.2

KST-Mixer: kinematic spatio-temporal data mixer for colon shape estimation Invited

Masahiro Oda, Kazuhiro Furukawa, Nassir Navab, Kensaku Mori

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Vol. 11 ( 4 ) page： 1 - 7 2023.1

人工知能（AI）の最新動向　RSNA2022におけるAI関連セッション Reviewed

インナービジョン Vol. 38 ( 2 ) page： 33 - 34 2023.1

L-former : A Lightweight Transformer for Realistic Medical Image Generation and its Application to Super-resolution Invited Reviewed

Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

MEDICAL IMAGING 2023 Vol. 12464 2023

Language：English Publishing type：Research paper (international conference proceedings) Publisher：SPIE-INT SOC OPTICAL ENGINEERING

Medical image analysis approaches such as data augmentation and domain adaption need huge amounts of realistic medical images. Generating realistic medical images by machine learning is a feasible approach. We propose L-former, a lightweight Transformer for realistic medical image generation. L-former can generate more reliable and realistic medical images than recent generative adversarial networks (GANs). Meanwhile, L-former does not consume as high computational cost as conventional Transformer-based generative models. L-former uses Transformers to generate low-resolution feature vectors at shallow layers, and uses convolutional neural networks to generate high-resolution realistic medical images at deep layers. Experimental results showed that L-former outperformed conventional GANs by FID scores 33.79 and 76.85 on two datasets, respectively. We further conducted a downstream study by using the images generated by L-former to perform a super-resolution task. A high PSNR score of 27.87 proved L-former's ability to generate reliable images for super-resolution and showed its potential for applications in medical diagnosis.

DOI： 10.1117/12.2653776

Web of Science

Scopus

Boundary-aware Feature and Prediction Refinement for Polyp Segmentation Reviewed

ie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori, ``Boundary-aware Feature and Prediction Refinement for Polyp Segmentation

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022.12

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning Invited Reviewed

Sota Kato, Masahiro Oda, Kensaku Mori, Akinobu Shimizu, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kazuhiro Hotta

Scientific Reports Vol. 12 ( 1 ) page： 20840 2022.12

Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC

Abstract

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

DOI： 10.1038/s41598-022-24936-6

Scopus

PubMed

Other Link： https://www.nature.com/articles/s41598-022-24936-6

Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation. Invited Reviewed International journal

Hayato Itoh, Masashi Misawa, Yuichi Mori, Shin-Ei Kudo, Masahiro Oda, Kensaku Mori

International journal of computer assisted radiology and surgery Vol. 17 ( 11 ) page： 2051 - 2063 2022.11

Language：English Publishing type：Research paper (scientific journal)

PURPOSE: Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a well-trained object detector, which achieves fast and accurate polyp detection with a bounding box towards a precise automated polyp localisation. METHOD: We refine gradient-weighted class activation mapping for more accurate highlighting of important patterns in processing a convolutional neural network. Extending the refined mapping into multiscaled processing, we define object activation mapping that highlights important object patterns in an image for a detection task. Finally, we define polyp activation mapping to achieve precise polyp localisation by integrating adaptive local thresholding into object activation mapping. We experimentally evaluate the proposed visual explaining methods with four publicly available databases. RESULTS: The refined mapping visualises important patterns in each convolutional layer more accurately than the original gradient-weighted class activation mapping. The object activation mapping clearly visualises important patterns in colonoscopic images for polyp detection. The polyp activation mapping localises the detected polyps in ETIS-Larib, CVC-Clinic and Kvasir-SEG database with mean Dice scores of 0.76, 0.72 and 0.72, respectively. CONCLUSIONS: We developed new visual explaining methods for a convolutional neural network by refining and extending gradient-weighted class activation mapping. Experimental results demonstrated the validity of the proposed methods by showing that accurate visualisation of important patterns and localisation of polyps in a colonoscopic image. The proposed visual explaining methods are useful for the interpreting and applying a trained polyp detector.

DOI： 10.1007/s11548-022-02696-y

Web of Science

Scopus

PubMed

Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI Reviewed

Hayato Itoh, Tao Hu, Masahiro Oda, Shinji Saiki, Koji Kamagata, Nobutaka Hattori, Shigeki Aoki,Kensaku Mori

LNCS13594 2022.10

Enhancing Model Generalization for Substantia Nigra Segmentation Using a Test-time Normalization-Based Method Reviewed

Tao Hu, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Zhongyang Lu, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kanako K. Kumamaru, Toshiaki Akashi, Kensaku Mori

LNCS13437 page： 736 - 744 2022.9

Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency Reviewed

Wenda Li, Yuichiro Hayashi, Masahiro Oda, akayuki Kitasaka, Kazunari Misawa, Kensaku Mori

LNCS13434 page： 467 - 477 2022.9

Confocal Laser Scanning Microscope Image Super Resolution for Biomedical Research Based on Two-Stage Generative Adversarial Network Invited Reviewed

Yunheng WU, Masahiro ODA, Yuichiro HAYASHI, Takanori TAKEBE, Shogo NAGATA, Shuntaro KAWAMURA, Kensaku MORI

page： 138 - 139 2022.7

Co-Training for Semi-Supervised CT Segmentation of COVID-19 Invited Reviewed

Kai LIU, Masahiro ODA, Tong ZHENG, Yuichiro HAYASHI, Yoshito OTAKE, Masahiro HASHIMOTO, Toshiaki AKASHI, Shigeki AOKI, Kensaku MORI

page： 114 - 115 2022.7

A Novel Centroid-attention based Hybrid Model for Subarachnoid Hemorrhage Classification on Imbalanced Data Invited Reviewed

Zhongyang LU, Masahiro ODA,1, Yuichiro HAYASHI, Tao Hu, Hayato ITOH, Takeyuki WATADANI, Osamu ABE，Kensaku MORI

page： 104 - 105 2022.7

テンソル分解を用いた黒質緻密部の3 次元パターン表現に関する初期的検討 Invited Reviewed

伊東隼人, 小田昌宏, 斉木臣二, 服部信考, 鎌形康司, 青木茂樹, 森健策

第41回日本医用画像工学会大会予稿集 page： 124 - 125 2022.7

境界情報を考慮する損失関数を用いたFCN による腹部 CT 像からの臓器領域抽出に関する研究 Invited Reviewed

大野真奈, 申忱, Holger R. Roth, 小田昌宏, 林雄一郎, 三澤一成, 森健策

第41回日本医用画像工学会大会予稿集 page： 106 - 107 2022.7

GNN による血管名自動命名手法における臓器特徴の利用に関する検討 Invited Reviewed

出口智也, 林雄一郎, 北坂孝幸, 小田昌宏, 三澤一成, 森健策

　第41回日本医用画像工学会大会予稿集 2022.7

大腸外科領域における情報支援内視鏡外科手術システムの開発 Invited Reviewed

]長谷川寛, 北口大地, 小島成浩, 竹下修由, 森健策, 伊藤雅昭

日本コンピュータ外科学会誌第31回日本コンピュータ外科学会大会特集号 Vol. 24 ( 2 ) 2022.6

　 PREV - NEXT 　

<MORI, Kensaku>

Papers - MORI, Kensaku