Adversarial Training and Dilated Convolutions for Brain MRI Segmentation Adversarial Training and Dilated Convolutions for Brain MRI Segmentation
Paper summary Problem ========= Brain MRI segmentation using adversarial training approach Dataset ====== 55 T1 weighted brain MR images (35 adults and 20 elders) with respective label maps. Contributions ========== 1. The authors suggest an adversarial loss in addition to the traditional loss. 2. The authors compare 2 Generator (Segmentor) models - Fully convolutional and dilated networks. https://i.imgur.com/orhWhoM.png Dilated network ------------------ Using conv layers, allows for larger receptive field with fewer trainable weights (compared to the FCN option). However, the authors claim the adversarial loss contributes more when applying the FCN model

Summary by Kirill Pevzner 6 months ago
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