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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

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


  • Anna Gummeson
  • Ida Arvidsson
  • Mattias Ohlsson
  • Niels C. Overgaard
  • Agnieszka Krzyzanowska
  • Anders Heyden
  • Anders Bjartell
  • Kalle Aström

Summary, in English

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.


  • ELLIIT: the Linköping-Lund initiative on IT and mobile communication
  • Centre for Mathematical Sciences
  • Computational Biology and Biological Physics - Has been reorganised
  • Department of Astronomy and Theoretical Physics - Has been reorganised
  • Department of Translational Medicine
  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
  • EpiHealth: Epidemiology for Health

Publishing year





Medical Imaging 2017: Digital Pathology



Document type

Conference paper




  • Cancer and Oncology
  • Other Computer and Information Science


  • Classification
  • Convolutional neural networks
  • Deep learning
  • Gleason grading
  • Prostate cancer

Conference name

Medical Imaging 2017: Digital Pathology

Conference date

2017-02-12 - 2017-02-13

Conference place

Orlando, United States




  • Lund University AI Research


  • ISBN: 9781510607255