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
  • [1] Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach
    Galatolo, Gabriele
    Papi, Matteo
    Spinelli, Andrea
    Giomi, Guglielmo
    Zedda, Andrea
    Calderisi, Marco
    DELTA: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS, 2022, : 102 - 109
  • [2] Deep learning-based fully automatic segmentation of wrist cartilage in MR images
    Brui, Ekaterina
    Efimtcev, Aleksandr Y.
    Fokin, Vladimir A.
    Fernandez, Remi
    Levchuk, Anatoliy G.
    Ogier, Augustin C.
    Samsonov, Alexey A.
    Mattei, Jean P.
    Melchakova, Irina V.
    Bendahan, David
    Andreychenko, Anna
    NMR IN BIOMEDICINE, 2020, 33 (08)
  • [3] Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach
    Balsiger, Fabian
    Steindel, Carolin
    Arn, Mirjam
    Wagner, Benedikt
    Grunder, Lorenz
    El-Koussy, Marwan
    Valenzuela, Waldo
    Reyes, Mauricio
    Scheidegger, Olivier
    FRONTIERS IN NEUROLOGY, 2018, 9
  • [4] Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area
    Van Erck, Dennis
    Moeskops, Pim
    Schoufour, Josje D.
    Weijs, Peter J. M.
    Scholte Op Reimer, Wilma J. M.
    Van Mourik, Martijn S.
    Janmaat, Yvonne C.
    Planken, R. Nils
    Vis, Marije
    Baan, Jan
    Hemke, Robert
    Isgum, Ivana
    Henriques, Jose P.
    De Vos, Bob D.
    Delewi, Ronak
    FRONTIERS IN NUTRITION, 2022, 9
  • [5] Deep Learning-based Approach to Predict Pulmonary Function at Chest CT
    Park, Hyunjung
    Yun, Jihye
    Lee, Sang Min
    Hwang, Hye Jeon
    Seo, Joon Beom
    Jung, Young Ju
    Hwang, Jeongeun
    Lee, Se Hee
    Lee, Sei Won
    Kim, Namkug
    RADIOLOGY, 2023, 307 (02)
  • [6] DRANet: Deep Learning-Based Automatic White Balancing Approach to CVCC
    Choi, Ho-Hyoung
    IEEE ACCESS, 2025, 13 : 36714 - 36722
  • [7] A Fully Automatic based Deep Learning Approach for Aneurysm Detection in DSA Images
    Rahmany, Ines
    Guetari, Ramzi
    Khlifa, Nawres
    2018 IEEE THIRD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, APPLICATIONS AND SYSTEMS (IPAS), 2018, : 303 - 307
  • [8] The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases
    Son, Hye Joo
    Oh, Jungsu S.
    Oh, Minyoung
    Kim, Soo Jong
    Lee, Jae-Hong
    Roh, Jee Hoon
    Kim, Jae Seung
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2020, 47 (02) : 332 - 341
  • [9] The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases
    Hye Joo Son
    Jungsu S. Oh
    Minyoung Oh
    Soo Jong Kim
    Jae-Hong Lee
    Jee Hoon Roh
    Jae Seung Kim
    European Journal of Nuclear Medicine and Molecular Imaging, 2020, 47 : 332 - 341
  • [10] Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs
    Hwang, Jae-Yeon
    Kim, Yisak
    Hwang, Jisun
    Suh, Yehyun
    Hwang, Sook Min
    Lee, Hyeyun
    Park, Minsu
    PEDIATRIC RADIOLOGY, 2024, : 1692 - 1703