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Automatic Gleason grading of H&E stained microscopic prostate images using deep convolutional neural networks

Gummeson, Anna; Arvidsson, Ida LU ; Ohlsson, Mattias LU ; Overgaard, Niels C. LU ; Krzyzanowska, Agnieszka LU ; Heyden, Anders LU ; Bjartell, Anders LU and Aström, Kalle LU (2017) Medical Imaging 2017: Digital Pathology In Medical Imaging 2017: Digital Pathology 10140.
Abstract

Prostate cancer is the most diagnosed cancer in men. The diagnosis is confirmed by pathologists based on ocular inspection of prostate biopsies in order to classify them according to Gleason score. The main goal of this paper is to automate the classification using convolutional neural networks (CNNs). The introduction of CNNs has broadened the field of pattern recognition. It replaces the classical way of designing and extracting hand-made features used for classification with the substantially different strategy of letting the computer itself decide which features are of importance. For automated prostate cancer classification into the classes: Benign, Gleason grade 3, 4 and 5 we propose a CNN with small convolutional filters that has... (More)

Prostate cancer is the most diagnosed cancer in men. The diagnosis is confirmed by pathologists based on ocular inspection of prostate biopsies in order to classify them according to Gleason score. The main goal of this paper is to automate the classification using convolutional neural networks (CNNs). The introduction of CNNs has broadened the field of pattern recognition. It replaces the classical way of designing and extracting hand-made features used for classification with the substantially different strategy of letting the computer itself decide which features are of importance. For automated prostate cancer classification into the classes: Benign, Gleason grade 3, 4 and 5 we propose a CNN with small convolutional filters that has been trained from scratch using stochastic gradient descent with momentum. The input consists of microscopic images of haematoxylin and eosin stained tissue, the output is a coarse segmentation into regions of the four different classes. The dataset used consists of 213 images, each considered to be of one class only. Using four-fold cross-validation we obtained an error rate of 7.3%, which is significantly better than previous state of the art using the same dataset. Although the dataset was rather small, good results were obtained. From this we conclude that CNN is a promising method for this problem. Future work includes obtaining a larger dataset, which potentially could diminish the error margin.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Classification, Convolutional neural networks, Deep learning, Gleason grading, Prostate cancer
in
Medical Imaging 2017: Digital Pathology
volume
10140
publisher
SPIE
conference name
Medical Imaging 2017: Digital Pathology
external identifiers
  • scopus:85020314166
  • wos:000404880200026
ISBN
9781510607255
DOI
10.1117/12.2253620
language
English
LU publication?
yes
id
85c3a0e7-18ce-4911-951b-fd95b0f2bdea
date added to LUP
2017-08-09 12:14:47
date last changed
2018-04-23 11:25:55
@inproceedings{85c3a0e7-18ce-4911-951b-fd95b0f2bdea,
  abstract     = {<p>Prostate cancer is the most diagnosed cancer in men. The diagnosis is confirmed by pathologists based on ocular inspection of prostate biopsies in order to classify them according to Gleason score. The main goal of this paper is to automate the classification using convolutional neural networks (CNNs). The introduction of CNNs has broadened the field of pattern recognition. It replaces the classical way of designing and extracting hand-made features used for classification with the substantially different strategy of letting the computer itself decide which features are of importance. For automated prostate cancer classification into the classes: Benign, Gleason grade 3, 4 and 5 we propose a CNN with small convolutional filters that has been trained from scratch using stochastic gradient descent with momentum. The input consists of microscopic images of haematoxylin and eosin stained tissue, the output is a coarse segmentation into regions of the four different classes. The dataset used consists of 213 images, each considered to be of one class only. Using four-fold cross-validation we obtained an error rate of 7.3%, which is significantly better than previous state of the art using the same dataset. Although the dataset was rather small, good results were obtained. From this we conclude that CNN is a promising method for this problem. Future work includes obtaining a larger dataset, which potentially could diminish the error margin.</p>},
  author       = {Gummeson, Anna and Arvidsson, Ida and Ohlsson, Mattias and Overgaard, Niels C. and Krzyzanowska, Agnieszka and Heyden, Anders and Bjartell, Anders and Aström, Kalle},
  booktitle    = {Medical Imaging 2017: Digital Pathology},
  isbn         = {9781510607255},
  keyword      = {Classification,Convolutional neural networks,Deep learning,Gleason grading,Prostate cancer},
  language     = {eng},
  publisher    = {SPIE},
  title        = {Automatic Gleason grading of H&E stained microscopic prostate images using deep convolutional neural networks},
  url          = {http://dx.doi.org/10.1117/12.2253620},
  volume       = {10140},
  year         = {2017},
}