Anatomical context protects deep learning from adversarial perturbations in medical imaging
| Autor: | Yi Li, Yifan Chen, Bennett A. Landman, Camilo Bermudez, Yevgeniy Vorobeychik, Huahong Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry Cognitive Neuroscience Deep learning Perturbation (astronomy) Image processing 02 engineering and technology Machine learning computer.software_genre Article Computer Science Applications Adversarial system 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Medical imaging 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Neurocomputing |
| ISSN: | 0925-2312 |
| Popis: | Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks are susceptible to small adversarial perturbations in the image. We study the impact of such adversarial perturbations in medical image processing where the goal is to predict an individual’s age based on a 3D MRI brain image. We consider two models: a conventional deep neural network, and a hybrid deep learning model which additionally uses features informed by anatomical context. We find that we can introduce significant errors in predicted age by adding imperceptible noise to an image, can accomplish this even for large batches of images using a single perturbation, and that the hybrid model is much more robust to adversarial perturbations than the conventional deep neural network. Our work highlights limitations of current deep learning techniques in clinical applications, and suggests a path forward. |
| Databáze: | OpenAIRE |
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