A multi-context CNN ensemble for small lesion detection
作者：
Highlights：
• We present a multi-context ensemble of convolutional neural networks for the automated detection of small lesions in medical images.
• The ensemble consists of different CNNs, aiming at learning different levels of image spatial context.
• Multiple-depth CNNs are individually trained on image patches of different dimensions and then combined together.
• We tested our method on microcalcification detection in mammograms and microaneurysm detection in fundus images.
• We obtained statistically significantly better detection performance with respect to other approaches in the literature.
摘要
•We present a multi-context ensemble of convolutional neural networks for the automated detection of small lesions in medical images.•The ensemble consists of different CNNs, aiming at learning different levels of image spatial context.•Multiple-depth CNNs are individually trained on image patches of different dimensions and then combined together.•We tested our method on microcalcification detection in mammograms and microaneurysm detection in fundus images.•We obtained statistically significantly better detection performance with respect to other approaches in the literature.
论文关键词：Ensemble classifier,Deep learning,Convolutional neural networks,Computer-aided detection (CADe),Mammograms,Ocular fundus images
论文评审过程：Received 28 April 2019, Revised 23 October 2019, Accepted 27 October 2019, Available online 13 November 2019, Version of Record 15 January 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2019.101749
