Literature DB >> 19163350

Automatic breast cancer grading of histopathological images.

Jean-Romain Dalle1, Wee Kheng Leow, Daniel Racoceanu, Adina Eunice Tutac, Thomas C Putti.   

Abstract

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.

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Year:  2008        PMID: 19163350     DOI: 10.1109/IEMBS.2008.4649847

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  16 in total

Review 1.  Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review.

Authors:  Asha Das; Madhu S Nair; S David Peter
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

2.  Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

Authors:  Kaustav Bera; Ian Katz; Anant Madabhushi
Journal:  JCO Clin Cancer Inform       Date:  2020-11

3.  Alteration in lipid composition differentiates breast cancer tissues: a 1H HRMAS NMR metabolomic study.

Authors:  Anup Paul; Surendra Kumar; Anubhav Raj; Abhinav A Sonkar; Sudha Jain; Atin Singhai; Raja Roy
Journal:  Metabolomics       Date:  2018-09-03       Impact factor: 4.290

Review 4.  Progress on deep learning in digital pathology of breast cancer: a narrative review.

Authors:  Jingjin Zhu; Mei Liu; Xiru Li
Journal:  Gland Surg       Date:  2022-04

5.  Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction.

Authors:  Xinyi Zhang; Shidan Wang; Erin R Rudzinski; Saloni Agarwal; Ruichen Rong; Donald A Barkauskas; Ovidiu Daescu; Lauren Furman Cline; Rajkumar Venkatramani; Yang Xie; Guanghua Xiao; Patrick Leavey
Journal:  Am J Pathol       Date:  2022-04-04       Impact factor: 5.770

6.  Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study.

Authors:  Federica Viti; Ivan Merelli; Mieke Timmermans; Michael den Bakker; Francesco Beltrame; Peter Riegman; Luciano Milanesi
Journal:  BMC Bioinformatics       Date:  2010-11-18       Impact factor: 3.169

7.  New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images.

Authors:  Jia-Mei Chen; Ai-Ping Qu; Lin-Wei Wang; Jing-Ping Yuan; Fang Yang; Qing-Ming Xiang; Ninu Maskey; Gui-Fang Yang; Juan Liu; Yan Li
Journal:  Sci Rep       Date:  2015-05-29       Impact factor: 4.379

8.  HyMaP: A hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images.

Authors:  Adnan M Khan; Hesham El-Daly; Emma Simmons; Nasir M Rajpoot
Journal:  J Pathol Inform       Date:  2013-03-30

9.  Automated image based prominent nucleoli detection.

Authors:  Choon K Yap; Emarene M Kalaw; Malay Singh; Kian T Chong; Danilo M Giron; Chao-Hui Huang; Li Cheng; Yan N Law; Hwee Kuan Lee
Journal:  J Pathol Inform       Date:  2015-06-23

10.  A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

Authors:  Akira Saito; Yasushi Numata; Takuya Hamada; Tomoyoshi Horisawa; Eric Cosatto; Hans-Peter Graf; Masahiko Kuroda; Yoichiro Yamamoto
Journal:  J Pathol Inform       Date:  2016-09-01
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