title: Mitosis detection in breast cancer histology images with deep neural networks.
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.
authors.affiliation: IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano, Switzerland. dan@idsia.ch
authors.forename: Dan C
authors.initials: DC
authors.lastname: Cireşan
authors.affiliation: IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano, Switzerland.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giusti
authors.affiliation: IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano, Switzerland.
authors.forename: Luca M
authors.initials: LM
authors.lastname: Gambardella
authors.affiliation: IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Lugano, Switzerland.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Schmidhuber
mesh_terms: D000465:Algorithms; D001706:Biopsy; D001943:Breast Neoplasms / Q000473:pathology* / Q000503:physiopathology*; D002467:Cell Nucleus / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008853:Microscopy / Q000379:methods*; D008938:Mitosis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity