Abstract
Generative Adversarial Network (GAN) has demonstrated great potentials in computer vision tasks such as image restoration. However, image restoration for specific scenarios, such as medical image enhancement is still facing challenge: How to ensure the visually plausible results while not containing hallucinated features that might jeopardize downstream tasks such as pathology identification? Here, we propose Task-GAN, a generalized model for medical reconstruction problem. A task-specific network that captures the diagnostic/pathology features, was added to couple the GAN based image reconstruction framework. Validated on multiple medical datasets, we demonstrated that the proposed method leads to improved deep learning based image reconstruction while preserving the detailed structure and diagnostic features.
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Ouyang, J., Wang, G., Gong, E., Chen, K., Pauly, J., Zaharchuk, G. (2019). Task-GAN: Improving Generative Adversarial Network for Image Reconstruction. In: Knoll, F., Maier, A., Rueckert, D., Ye, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2019. Lecture Notes in Computer Science(), vol 11905. Springer, Cham. https://doi.org/10.1007/978-3-030-33843-5_18
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DOI: https://doi.org/10.1007/978-3-030-33843-5_18
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