Can Pretrained Neural Networks Detect Anatomy?
This work addresses data scarcity in medical imaging, but it is incremental as it applies existing methods to a new domain.
The paper tackles the challenge of scarce annotated data in medical imaging by using pretrained networks as feature extractors and deep architectures with heavy augmentation and regularization, achieving results on X-ray images for anatomy detection.
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.