Deep learning enables fully automated mitotic density assessment in breast cancer histopathology

被引：0
|
作者：
Balkenhol, M. [1 ]
Bult, P. [1 ]
Tellez, D. [1 ]
Vreuls, W. [2 ]
Clahsen, P. [3 ]
Ciompi, F. [1 ]
Van der Laak, J. [1 ]
机构：
[1] Radboud Univ Nijmegen, Med Ctr, Pathol, Nijmegen, Netherlands
[2] Canisius Wilhelmina Hosp, Pathol, Nijmegen, Netherlands
[3] Haaglanden Med Ctr, Pathol, The Hague, Netherlands
来源：
EUROPEAN JOURNAL OF CANCER | 2020年 / 138卷
关键词：
D O I：
暂无
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
340
引用
收藏
页码：S86 / S86
页数：1
相关论文
共 50 条
  • [1] Deep learning enables fully automated mitotic density assessment in breast cancer histopathology
    Balkenhol, M.
    Bult, P.
    Tellez, D.
    Vreuls, W.
    Clahsen, P.
    Ciompi, F.
    van der Laak, J.
    VIRCHOWS ARCHIV, 2019, 475 : S58 - S59
  • [2] Fully Automated Breast Density Segmentation and Classification Using Deep Learning
    Saffari, Nasibeh
    Rashwan, Hatem A.
    Abdel-Nasser, Mohamed
    Kumar Singh, Vivek
    Arenas, Meritxell
    Mangina, Eleni
    Herrera, Blas
    Puig, Domenec
    DIAGNOSTICS, 2020, 10 (11)
  • [3] Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
    Mercan, Caner
    Balkenhol, Maschenka
    Salgado, Roberto
    Sherman, Mark
    Vielh, Philippe
    Vreuls, Willem
    Polonia, Antonio
    Horlings, Hugo M.
    Weichert, Wilko
    Carter, Jodi M.
    Bult, Peter
    Christgen, Matthias
    Denkert, Carsten
    van de Vijver, Koen
    Bokhorst, John-Melle
    van der Laak, Jeroen
    Ciompi, Francesco
    NPJ BREAST CANCER, 2022, 8 (01)
  • [4] Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
    Caner Mercan
    Maschenka Balkenhol
    Roberto Salgado
    Mark Sherman
    Philippe Vielh
    Willem Vreuls
    António Polónia
    Hugo M. Horlings
    Wilko Weichert
    Jodi M. Carter
    Peter Bult
    Matthias Christgen
    Carsten Denkert
    Koen van de Vijver
    John-Melle Bokhorst
    Jeroen van der Laak
    Francesco Ciompi
    npj Breast Cancer, 8
  • [5] Deep learning-based automated mitosis detection in histopathology images for breast cancer grading
    Mathew, Tojo
    Ajith, B.
    Kini, Jyoti R.
    Rajan, Jeny
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2022, 32 (04) : 1192 - 1208
  • [6] Deep learning model for fully automated breast cancer detection system from thermograms
    Mohamed, Esraa A.
    Rashed, Essam A.
    Gaber, Tarek
    Karam, Omar
    PLOS ONE, 2022, 17 (01):
  • [7] Deep learning assisted mitotic counting for breast cancer
    Balkenhol, Maschenka C. A.
    Tellez, David
    Vreuls, Willem
    Clahsen, Pieter C.
    Pinckaers, Hans
    Ciompi, Francesco
    Bult, Peter
    van der Laak, Jeroen A. W. M.
    LABORATORY INVESTIGATION, 2019, 99 (11) : 1596 - 1606
  • [8] Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
    Hyun-Jong Jang
    Ahwon Lee
    Jun Kang
    In Hye Song
    Sung Hak Lee
    World Journal of Gastroenterology, 2021, (44) : 7687 - 7704
  • [9] Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
    Jang, Hyun-Jong
    Lee, Ahwon
    Kang, Jun
    Song, In Hye
    Lee, Sung Hak
    WORLD JOURNAL OF GASTROENTEROLOGY, 2021, 27 (44) : 7687 - 7704
  • [10] Assessment of deep learning technique for fully automated mandibular segmentation
    Yurdakurban, Ebru
    Sukut, Yagizalp
    Duran, Gokhan Serhat
    AMERICAN JOURNAL OF ORTHODONTICS AND DENTOFACIAL ORTHOPEDICS, 2025, 167 (02) : 242 - 249
12345