Automatic Breast Cancer Grading of Histopathological Images

被引：58
|
作者：
Dalle, Jean-Romain [1 ]
Leow, Wee Kheng [1 ]
Racoceanu, Daniel [2 ]
Tutac, Adina Eunice [3 ]
Putti, Thomas C. [4 ]
机构：
[1] Natl Univ Singapore, Dept Comp Sci, Comp 1, Singapore 117590, Singapore
[2] IPAL, CNRS, UMI 2955, Singapore 119613, Singapore
[3] Univ Politecn Timisoara, Dept Engn, Timisoara, Romania
[4] Natl Univ Singapore Hosp, Dept Pathol, Singapore 119074, Singapore
来源：
2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IEMBS.2008.4649847
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Breast cancer grading of histopathological images is the standard clinical practice for the diagnosis and prognosis of breast cancer development. In a large hospital, a pathologist typically handles 100 grading cases per day, each consisting of about 2000 image frames. It is, therefore, a very tedious and time-consuming task. This paper proposes a method for automatic computer grading to assist pathologists by providing second opinions and reducing their workload. It combines the three criteria in the Nottingham scoring system using a multi-resolution approach. To our best knowledge, there is no existing work that provide complete grading according to the Nottingham criteria.
引用
收藏
页码：3052 / +
页数：3
相关论文
共 50 条
  • [41] A CONVOLUTIONAL NEURAL NETWORK TO PREDICT BREAST CANCER FOR HISTOPATHOLOGICAL IMAGES
    Nagar, Sonika
    Jain, Mradul Kumar
    Nirvikar
    Agarwal, Amit Kumar
    ADVANCES AND APPLICATIONS IN MATHEMATICAL SCIENCES, 2021, 20 (03): : 409 - 426
  • [42] Evaluation of Histopathological Images Segmentation Techniques for Breast Cancer Detection
    Baker, Qanita Bani
    Abu Qutaish, Ala'a
    2021 12TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 2021, : 134 - 139
  • [43] Classifier Result Aggregation for Automatically Grading Histopathological Images
    Stoean, Catalin
    Lichtblau, Daniel
    2017 19TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING (SYNASC 2017), 2017, : 419 - 425
  • [44] RELEVANCE OF HISTOPATHOLOGICAL GRADING IN COLON CANCER
    MENTGES, B
    RUMPELT, HJ
    BRUCKNER, R
    GRUSSNER, R
    RISSEEISSFELLER, EM
    CHIRURG, 1988, 59 (06): : 425 - 431
  • [45] Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study
    Yifeng Shi
    Linnea T. Olsson
    Katherine A. Hoadley
    Benjamin C. Calhoun
    J. S. Marron
    Joseph Geradts
    Marc Niethammer
    Melissa A. Troester
    npj Breast Cancer, 9
  • [46] Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study
    Shi, Yifeng
    Olsson, Linnea T.
    Hoadley, Katherine A.
    Calhoun, Benjamin C.
    Marron, J. S.
    Geradts, Joseph
    Niethammer, Marc
    Troester, Melissa A.
    NPJ BREAST CANCER, 2023, 9 (01)
  • [47] BIOPTIC GRADING, HISTOPATHOLOGICAL GRADING AND STAGING IN COLORECTAL-CANCER
    BUCCI, L
    SALFI, R
    NEBBIOSO, G
    MAZZEO, F
    JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH, 1984, 3 (04) : 475 - 478
  • [48] A cognitive approach to microscopy analysis applied to automatic breast cancer grading
    Roux, L.
    Tutac, A.
    Veillard, A.
    Dalle, J.
    Racoceanu, D.
    Lomenie, N.
    Klossa, J.
    VIRCHOWS ARCHIV, 2009, 455 : 34 - 35
  • [49] Automatic Glandular and Tubule Region Segmentation in Histological Grading of Breast Cancer
    Kien Nguyen
    Barnes, Michael
    Srinivas, Chukka
    Chefd'hotel, Christophe
    MEDICAL IMAGING 2015: DIGITAL PATHOLOGY, 2015, 9420
  • [50] Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment
    Luz, Daniel S.
    Lima, Thiago J. B.
    Silva, Romuere R. V.
    Magalhaes, Deborah M. V.
    Araujo, Flavio H. D.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 75
12345