research-article

Enhanced Random Forest for Mitosis Detection

Authors:

Angshuman Paul,

Dipti Prasad MukherjeeAuthors Info & Claims

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

Article No.: 85, Pages 1 - 8

https://doi.org/10.1145/2683483.2683569

Published: 14 December 2014 Publication History

Abstract

Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an insight to the growth of the cancer and helps in developing individual treatment plans. The Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological slides. Pathologists manually identify mitotic figures from a few thousand slide images for each patient to determine the grade of the cancer. Mitotic figures are hard to identify as the appearance of the mitotic cells change at different phases of mitosis. So, the manual cancer grading is not only a tedious job but also prone to observer variability. We propose a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees. The random trees are assigned a tree penalty and a forest penalty depending on their classification performance at the training phase. The weight of a tree is calculated based on these penalties. The forest is trained through regeneration of population from weighted trees. The input data is classified based on weighted voting from the random trees after several populations. Experiments show at least 11 percent improvement in F1 score on more than 450 histopathological images at ×40 magnification.

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Cited By

Jain VPhophalia A(2020)M-ary Random Forest - A new multidimensional partitioning approach to Random ForestMultimedia Tools and Applications10.1007/s11042-020-10047-9Online publication date: 30-Oct-2020
https://doi.org/10.1007/s11042-020-10047-9
Jain VSharma JSinghal KPhophalia A(2019)Exponentially Weighted Random ForestPattern Recognition and Machine Intelligence10.1007/978-3-030-34869-4_19(170-178)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-34869-4_19
Paul AGangopadhyay AChintha AMukherjee DDas PKundu S(2018)Calculation of phase fraction in steel microstructure images using random forest classifierIET Image Processing10.1049/iet-ipr.2017.115412:8(1370-1377)Online publication date: Aug-2018
https://doi.org/10.1049/iet-ipr.2017.1154
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Index Terms

Enhanced Random Forest for Mitosis Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Other conferences

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

December 2014

692 pages

ISBN:9781450330619

DOI:10.1145/2683483

General Chairs:
A. G. Ramakrishnan
IISc, Bangalore
,
Jitendra Malik
University California, Berkeley
,
Program Chairs:
Alex Efros
UC-Berkeley
,
C. V. Jawahar
IIIT Hyderabad
,
Manik Varma
Microsoft Research

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2014

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ICVGIP '14

ICVGIP '14: Indian Conference on Computer Vision Graphics and Image Processing

December 14 - 18, 2014

Bangalore, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Jain VPhophalia A(2020)M-ary Random Forest - A new multidimensional partitioning approach to Random ForestMultimedia Tools and Applications10.1007/s11042-020-10047-9Online publication date: 30-Oct-2020
https://doi.org/10.1007/s11042-020-10047-9
Jain VSharma JSinghal KPhophalia A(2019)Exponentially Weighted Random ForestPattern Recognition and Machine Intelligence10.1007/978-3-030-34869-4_19(170-178)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-34869-4_19
Paul AGangopadhyay AChintha AMukherjee DDas PKundu S(2018)Calculation of phase fraction in steel microstructure images using random forest classifierIET Image Processing10.1049/iet-ipr.2017.115412:8(1370-1377)Online publication date: Aug-2018
https://doi.org/10.1049/iet-ipr.2017.1154
Das DMitra PChakraborty CChatterjee SMaiti ABose S(2017)Computational approach for mitotic cell detection and its application in oral squamous cell carcinomaMultidimensional Systems and Signal Processing10.1007/s11045-017-0488-628:3(1031-1050)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s11045-017-0488-6
Paul AMukherjee DBatra DBrown MNatarajan V(2016)Reinforced random forestProceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3009977.3010003(1-8)Online publication date: 18-Dec-2016
https://dl.acm.org/doi/10.1145/3009977.3010003
Paul ADey AMukherjee DSivaswamy JTourani V(2015)Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer ImagesMedical Image Computing and Computer-Assisted Intervention -- MICCAI 201510.1007/978-3-319-24571-3_12(94-102)Online publication date: 20-Nov-2015
https://doi.org/10.1007/978-3-319-24571-3_12

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