Can Pretrained Neural Networks Detect Anatomy?

2015/12/18 by Vlado Menkovski, Menkovski, Vlado, Zharko Aleksovski +5
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.CV #cs.NE

paper · pdf · doi:10.48550/arxiv.1512.05986

NIPS 2015 Workshop on Machine Learning in Healthcare

arxiv created 2015/12/18 · arxiv updated 2015/12/21

Abstract

Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant. In medical imaging, there is a huge variety of possible imaging modalities and contrasts, where annotated data is usually very scarce. We present two approaches to deal with this challenge. A network pretrained in a different domain with abundant data is used as a feature extractor, while a subsequent classifier is trained on a small target dataset; and a deep architecture trained with heavy augmentation and equipped with sophisticated regularization methods. We test the approaches on a corpus of X-ray images to design an anatomy detection system.

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