Abstract
We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin. © 2013 Springer-Verlag.
Original language | English (US) |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Pages | 411-418 |
Number of pages | 8 |
DOIs | |
State | Published - Oct 24 2013 |
Externally published | Yes |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science