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Large-scale retrieval for medical image analytics: A comprehensive review
Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …
digital imaging techniques, where huge amounts of medical images were produced with …
Computational health informatics in the big data age: a survey
The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …
of research activity in the healthcare and data sciences fields. The conventional approaches …
Image matching from handcrafted to deep features: A survey
As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …
then correspond the same or similar structure/content from two or more images. Over the …
A decade survey of content based image retrieval using deep learning
The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …
against a query image. Generally, the similarity between the representative features of the …
Deep multi-view enhancement hashing for image retrieval
Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …
embedding high-dimensional feature descriptors into a similarity preserving Hamming …
Deep fuzzy hashing network for efficient image retrieval
Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …
images to semantically correlated binary codes in the Hamming space with similarity well …
Central similarity quantization for efficient image and video retrieval
Existing data-dependent hashing methods usually learn hash functions from pairwise or
triplet data relationships, which only capture the data similarity locally, and often suffer from …
triplet data relationships, which only capture the data similarity locally, and often suffer from …
One loss for all: Deep hashing with a single cosine similarity based learning objective
A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …
binary hash codes discriminative and to minimize a quantization error. With further …
Self-supervised adversarial hashing networks for cross-modal retrieval
Thanks to the success of deep learning, cross-modal retrieval has made significant progress
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …
Hashnet: Deep learning to hash by continuation
Learning to hash has been widely applied to approximate nearest neighbor search for large-
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …