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eess.IV2021
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Zixuan Liu, Ehsan Adeli, Kilian M. Pohl +1
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision pr…
eess.IV2021★ 9 cited
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
Xiaocong Chen, Yun Li, Lina Yao +2
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. Without a sufficient number of training samples, deep learning ba…