Fig. 1.

Proposed deep disentangled momentum hashing (DDMH) framework for neuroimage search, including three key components: (1) a disentangled triplet loss, which improves the traditional triplet loss by decoupling it from the hash code length; (2) a cross-entropy loss, which is used to optimize the hash codes by jointly learning a linear classifier; (3) a momentum triplet strategy, which builds a large and consistent dictionary on-the-fly and enables efficient triplet-based learning even with a small batch-size.