Deep learning-based fully automatic approach to predict amyloid PET centiloid scales

被引:0
|
作者
Yamao, Tensho [1 ]
Miwa, Kenta [1 ]
Kaneko, Yuta [2 ]
Takahashi, Noriyuki [1 ]
Miyaji, Noriaki [3 ]
Ito, Hiroshi [4 ]
Matsuda, Hiroshi [1 ]
机构
[1] Fukushima Med Univ, Fukushima, Japan
[2] Int Univ Hlth & Welf, Otawara, Japan
[3] Japanese Fdn Canc Res, Canc Inst Hosp, Tokyo, Japan
[4] Fukushima Med Univ, Dept Radiol & Nucl Med, Fukushima, Japan
关键词
D O I
暂无
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
P517
引用
收藏
页数:3
相关论文
共 50 条
  • [41] Deep learning-based fully automatic screening of carotid artery plaques in computed tomography angiography: a multicenter study
    Zhai, D.
    Liu, R.
    Liu, Y.
    Yin, H.
    Tang, W.
    Yang, J.
    Liu, K.
    Fan, G.
    Ju, S.
    Cai, W.
    CLINICAL RADIOLOGY, 2024, 79 (08) : e994 - e1002
  • [42] Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap
    Youn, Sangyeon
    Lee, Kyungsu
    Son, Jeehoon
    Yang, In-Hwan
    Hwang, Jae Youn
    BIOMEDICAL OPTICS EXPRESS, 2020, 11 (06): : 2976 - 2995
  • [43] Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images
    Hanseung Choi
    Kug Jin Jeon
    Young Hyun Kim
    Eun-Gyu Ha
    Chena Lee
    Sang-Sun Han
    Scientific Reports, 12
  • [44] Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images
    Choi, Hanseung
    Jeon, Kug Jin
    Kim, Young Hyun
    Ha, Eun-Gyu
    Lee, Chena
    Han, Sang-Sun
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [45] Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings
    Zulfiqar, Hasan
    Guo, Zhiling
    Ahmad, Ramala Masood
    Ahmed, Zahoor
    Cai, Peiling
    Chen, Xiang
    Zhang, Yang
    Lin, Hao
    Shi, Zheng
    FRONTIERS IN MEDICINE, 2024, 10
  • [46] A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy
    Tanaka, Shohei
    Kadoya, Noriyuki
    Sugai, Yuto
    Umeda, Mariko
    Ishizawa, Miyu
    Katsuta, Yoshiyuki
    Ito, Kengo
    Takeda, Ken
    Jingu, Keiichi
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [47] Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis
    Qi, Jing
    Ruan, Guangcong
    Ping, Yi
    Xiao, Zhifeng
    Liu, Kaijun
    Cheng, Yi
    Liu, Rongbei
    Zhang, Bingqiang
    Zhi, Min
    Chen, Junrong
    Xiao, Fang
    Zhao, Tingting
    Li, Jiaxing
    Zhang, Zhou
    Zou, Yuxin
    Cao, Qian
    Nian, Yongjian
    Wei, Yanling
    THERAPEUTIC ADVANCES IN GASTROENTEROLOGY, 2023, 16
  • [48] Deep learning-based PET image denoising and reconstruction: a review
    Hashimoto, Fumio
    Onishi, Yuya
    Ote, Kibo
    Tashima, Hideaki
    Reader, Andrew J.
    Yamaya, Taiga
    RADIOLOGICAL PHYSICS AND TECHNOLOGY, 2024, 17 (01) : 24 - 46
  • [49] A Deep Learning-Based Multimodal Architecture to predict Signs of Dementia
    Ortiz-Perez, David
    Ruiz-Ponce, Pablo
    Tomas, David
    Garcia-Rodriguez, Jose
    Vizcaya-Moreno, M. Flores
    Leo, Marco
    NEUROCOMPUTING, 2023, 548
  • [50] A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy
    Shohei Tanaka
    Noriyuki Kadoya
    Yuto Sugai
    Mariko Umeda
    Miyu Ishizawa
    Yoshiyuki Katsuta
    Kengo Ito
    Ken Takeda
    Keiichi Jingu
    Scientific Reports, 12