Combating Ambiguity for Hash-Code Learning in Medical Instance Retrieval

Combating Ambiguity for Hash-Code Learning in Medical Instance Retrieval

When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by v...

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Bibliographic Details
Main Authors: Fang, J. (Author), Fu, H. (Author), Liu, J. (Author), Yan, X. (Author), Yan, Y. (Author), Zeng, D. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
accuracy
algorithm
Algorithms
Alzheimer disease
Article
Classification (of information)
classification algorithm
Codes (symbols)
content-based image retrieval
Convolution
convolutional features
cryptococcosis
Databases, Factual
deep hashing methods
Diagnosis
diagnostic imaging
Discriminative features
electroencephalography
entropy
factual database
Feature aggregation
glaucoma
Hash functions
hematoma
human
Humans
Image coding
image reconstruction
image retrieval
image segmentation
Instance retrieval
Large image database
learning
learning algorithm
machine learning
Medical imaging
Medical instance retrieval
methodology
Military photography
optic disk
Query processing
radiologist
Radiologists
receptive field
Research Design
Retrieval performance
Semantic information
semantics
Semantics
Spatial differences
State-of-the-art methods
support vector machine
training
visual field
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