Deep learning for magnification independent breast cancer histopathology image classification

Neslihan Bayramoglu, Juho Kannala, Janne Heikkila

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

332 Citations (Scopus)


Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
Number of pages6
ISBN (Electronic)9781509048472
Publication statusPublished - 13 Apr 2017
MoE publication typeA4 Conference publication
EventInternational Conference on Pattern Recognition - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23

Publication series

NameInternational Conference on Pattern Recognition
ISSN (Electronic)1051-4651


ConferenceInternational Conference on Pattern Recognition
Abbreviated titleICPR


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