We propose a novel deeply learnt convolutional neural network architecture for supervised
hashing of medical images through residual learning, coined as Deep Residual Hashing …

With the rapid progress of medical image technology, medical image retrieval has attracted
wide attention in medical data processing fields. Deep hashing methods have been proven …

Hashing has been an important and effective technology in image retrieval due to its
computational efficiency and fast search speed. The traditional hashing methods usually …

In this paper, we propose an order-sensitive deep hashing for scalable medical image
retrieval in the scenario of coexistence of multiple medical conditions. The pairwise similarity …

The hashing method is widely used for large-scale image retrieval due to its low time and
space complexity. However, the existing deep hashing methods are mainly designed for …

The extraction of informative features from medical images and the retrieving of similar
images from data repositories is vital for clinical decision support systems. Unlike general …

Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …

Benefiting from low storage costs and high retrieval efficiency, hash learning has been a
widely adopted technology for approximating nearest neighbor in large-scale data retrieval …

Image hashing based on deep convolutional neural networks (CNN), deep hashing, has
acquired breakthrough in image retrieval. Although deep features from various CNN layers …

While transformers have indeed improved image retrieval accuracy in computer vision,
challenges persist, including insufficient and imbalanced feature extraction and the inability …