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of 275
pro vyhledávání: '"Tanaka, Kiyohito"'
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the in
Externí odkaz:
http://arxiv.org/abs/2411.14750
Severity level estimation is a crucial task in medical image diagnosis. However, accurately assigning severity class labels to individual images is very costly and challenging. Consequently, the attached labels tend to be noisy. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2410.21885
Publikováno v:
Medical Image Analysis 2024
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training dat
Externí odkaz:
http://arxiv.org/abs/2409.04952
Autor:
Zhao Jinyu, Yue Ping, Mi Ningning, Li Matu, Fu Wenkang, Zhang Xianzhuo, Gao Long, Bai Mingzhen, Tian Liang, Jiang Ningzu, Lu Yawen, Ma Haidong, Dong Chunlu, Zhang Yong, Zhang Hengwei, Zhang Jinduo, Ren Yanxian, Suzuki Azumi, Wong Peng F., Tanaka Kiyohito, Rerknimitr Rungsun, Junger Henrik H., Cheung Tan T., Melloul Emmanuel, Demartines Nicolas, Leung Joseph W., Yao Jia, Yuan Jinqiu, Lin Yanyan, Schlitt Hans J., Meng Wenbo
Publikováno v:
Medical Review, Vol 4, Iss 4, Pp 326-365 (2024)
Fibrosis resulting from pathological repair secondary to recurrent or persistent tissue damage often leads to organ failure and mortality. Biliary fibrosis is a crucial but easily neglected pathological feature in hepatobiliary disorders, which may p
Externí odkaz:
https://doaj.org/article/f00ec880e3164f95b5225644f4d94ec7
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a pos
Externí odkaz:
http://arxiv.org/abs/2302.12482
Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with ambiguous severity. An easier alternative is to use relative annotation,
Externí odkaz:
http://arxiv.org/abs/2208.03020
Autor:
Oda, Masahiro, Tanaka, Kiyohito, Takabatake, Hirotsugu, Mori, Masaki, Natori, Hiroshi, Mori, Kensaku
Publikováno v:
Healthcare Technology Letters, Vol.6, No.6, pp.214-219, 2019
This paper proposes a realistic image generation method for visualization in endoscopic simulation systems. Endoscopic diagnosis and treatment are performed in many hospitals. To reduce complications related to endoscope insertions, endoscopic simula
Externí odkaz:
http://arxiv.org/abs/2201.04918
Autor:
Oda, Masahiro, Itoh, Hayato, Tanaka, Kiyohito, Takabatake, Hirotsugu, Mori, Masaki, Natori, Hiroshi, Mori, Kensaku
Publikováno v:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale edge loss. We employ a two-step estimation process including Lambertia
Externí odkaz:
http://arxiv.org/abs/2201.04485
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties. First, endoscopic images with the annotation about UC (positive or negative) are usually limited. Second, they show a large v
Externí odkaz:
http://arxiv.org/abs/2111.03815
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