Adversarial Policy Gradient for Deep Learning Image Augmentation
| Autor: | Justin D. Krogue, Francesco Caliva, Sharmila Majumdar, Claudia Iriondo, Valentina Pedoia, Kaiyang Cheng |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Variance (accounting) Machine learning computer.software_genre Image (mathematics) Task (project management) Medical imaging Reinforcement learning Segmentation Noise (video) Artificial intelligence business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030322250 MICCAI (6) |
| DOI: | 10.1007/978-3-030-32226-7_50 |
| Popis: | The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset. However, implementing this approach with classical methods is challenging: the cost of obtaining a dense segmentation is high, and the precise input area that is most crucial to the classification task is difficult to determine a-priori. We propose a novel joint-training deep reinforcement learning framework for image augmentation. A segmentation network, weakly supervised with policy gradient optimization, acts as an agent, and outputs masks as actions given samples as states, with the goal of maximizing reward signals from the classification network. In this way, the segmentation network learns to mask unimportant imaging features. Our method, Adversarial Policy Gradient Augmentation (APGA), shows promising results on Stanford’s MURA dataset and on a hip fracture classification task with an increase in global accuracy of up to 7.33% and improved performance over baseline methods in 9/10 tasks evaluated. We discuss the broad applicability of our joint training strategy to a variety of medical imaging tasks. |
| Databáze: | OpenAIRE |
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