Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks
作者：
Highlights：
• Commonly studied scenario considers only binary cancer vs. no cancer classification.
• Our system classifies whole slide breast biopsies into five diagnostic categories.
• Pipeline of fully convolutional networks localizes diagnostically relevant regions.
• Convolutional neural network classifies detected regions of interest in whole slides.
• Experiments show that our method is compatible with predictions of 45 pathologists.
摘要
•Commonly studied scenario considers only binary cancer vs. no cancer classification.•Our system classifies whole slide breast biopsies into five diagnostic categories.•Pipeline of fully convolutional networks localizes diagnostically relevant regions.•Convolutional neural network classifies detected regions of interest in whole slides.•Experiments show that our method is compatible with predictions of 45 pathologists.
论文关键词：Digital pathology,Breast histopathology,Whole slide imaging,Region of interest detection,Saliency detection,Multi-class classification,Deep learning
论文评审过程：Received 22 August 2017, Revised 13 May 2018, Accepted 16 July 2018, Available online 20 July 2018, Version of Record 1 August 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.07.022
