Hashing with residual networks for image retrieval
Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th …, 2017•Springer
We propose a novel deeply learnt convolutional neural network architecture for supervised
hashing of medical images through residual learning, coined as Deep Residual Hashing
(DRH). It offers maximal separability of classes in hashing space while preserving semantic
similarities in local embedding neighborhoods. We also introduce a new optimization
formulation comprising of complementary loss terms and regularizations that suit hashing
objectives the best by controlling over quantization errors. We conduct extensive validations …
hashing of medical images through residual learning, coined as Deep Residual Hashing
(DRH). It offers maximal separability of classes in hashing space while preserving semantic
similarities in local embedding neighborhoods. We also introduce a new optimization
formulation comprising of complementary loss terms and regularizations that suit hashing
objectives the best by controlling over quantization errors. We conduct extensive validations …
Abstract
We propose a novel deeply learnt convolutional neural network architecture for supervised hashing of medical images through residual learning, coined as Deep Residual Hashing (DRH). It offers maximal separability of classes in hashing space while preserving semantic similarities in local embedding neighborhoods. We also introduce a new optimization formulation comprising of complementary loss terms and regularizations that suit hashing objectives the best by controlling over quantization errors. We conduct extensive validations on 2,599 Chest X-ray images with co-morbidities against eight state-of-the-art hashing techniques and demonstrate improved performance and computational benefits of the proposed algorithm for fast and scalable retrieval.
Springer
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