Versatile anomaly detection method for medical images with semi-supervised flow-based generative models

被引：0
|
作者：
Hisaichi Shibata
Shouhei Hanaoka
Yukihiro Nomura
Takahiro Nakao
Issei Sato
Daisuke Sato
Naoto Hayashi
Osamu Abe
机构：
[1] The University of Tokyo Hospital,Department of Computational Diagnostic Radiology and Preventive Medicine
[2] The University of Tokyo Hospital,Department of Radiology
[3] The University of Tokyo,Department of Computer Science, Graduate School of Information Science and Technology
[4] RIKEN,Center for Advanced Intelligence Project
[5] The University of Tokyo,Division of Radiology and Biomedical Engineering, Graduate School of Medicine
来源：
International Journal of Computer Assisted Radiology and Surgery | 2021年 / 16卷
关键词：
Anomaly detection; Brain computed tomography; Chest X-ray; Deep learning; Semi-supervised;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：2261 / 2267
页数：6
相关论文
共 50 条
  • [1] Versatile anomaly detection method for medical images with semi-supervised flow-based generative models
    Shibata, Hisaichi
    Hanaoka, Shouhei
    Nomura, Yukihiro
    Nakao, Takahiro
    Sato, Issei
    Sato, Daisuke
    Hayashi, Naoto
    Abe, Osamu
    [J]. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2021, 16 (12) : 2261 - 2267
  • [2] Flow-based anomaly detection using semi-supervised learning
    Jadidi, Zahra
    Muthukkumarasamy, Vallipuram
    Sithirasenan, Elankayer
    Singh, Kalvinder
    [J]. 2015 9TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ICSPCS), 2015,
  • [3] Semi-Supervised Anomaly Detection Based on Deep Generative Models with Transformer
    Shangguan, Weimin
    Fan, Wentao
    Chen, Ziyi
    [J]. 6TH INTERNATIONAL CONFERENCE ON INNOVATION IN ARTIFICIAL INTELLIGENCE, ICIAI2022, 2022, : 172 - 177
  • [4] A Semi-supervised AUC Optimization Method with Generative Models
    Fujino, Akinori
    Ueda, Naonori
    [J]. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2016, : 883 - 888
  • [5] Semi-supervised community detection method based on generative adversarial networks
    Liu, Xiaoyang
    Zhang, Mengyao
    Liu, Yanfei
    Liu, Chao
    Li, Chaorong
    Wang, Wei
    Zhang, Xiaoqin
    Bouyer, Asgarali
    [J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2024, 36 (03)
  • [6] DISCRIMINATIVE SEMI-SUPERVISED GENERATIVE ADVERSARIAL NETWORK FOR HYPERSPECTRAL ANOMALY DETECTION
    Jiang, Tao
    Xie, Weiying
    Li, Yunsong
    Du, Qian
    [J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 2420 - 2423
  • [7] Semi-supervised Sequential Generative Models
    Teng, Michael
    Le, Tuan Anh
    Scibior, Adam
    Wood, Frank
    [J]. CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020), 2020, 124 : 649 - 658
  • [8] Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images
    Kumar, Nitin
    Awate, Suyash P.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 4772 - 4787
  • [9] Network anomaly detection based on semi-supervised clustering
    Wei Xiaotao
    Huang Houkuan
    Tian Shengfeng
    [J]. NEW ADVANCES IN SIMULATION, MODELLING AND OPTIMIZATION (SMO '07), 2007, : 440 - +
  • [10] High-quality semi-supervised anomaly detection with generative adversarial networks
    Sato, Yuki
    Sato, Junya
    Tomiyama, Noriyuki
    Kido, Shoji
    [J]. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2023,
12345