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Large-scale retrieval for medical image analytics: A comprehensive review.
Zhongyu Li1, Xiaofan Zhang1, Henning Müller2
1Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Medical Image Analysis
|October 17, 2017
Summary
This review explores advanced computer vision and machine learning techniques for analyzing large-scale medical image data. It covers methods for feature representation, indexing, and searching to improve medical image analytics and retrieval.
Area of Science:
- Medical image analysis
- Computer vision
- Machine learning
- Information retrieval
Background:
- Digital imaging has led to vast increases in medical image data.
- Conventional analysis methods struggle with the scale and complexity of modern medical images.
Purpose of the Study:
- To review state-of-the-art approaches for large-scale medical image analysis.
- To summarize challenges and opportunities in analyzing extensive medical image datasets.
- To provide a comprehensive overview of algorithms and techniques for large-scale medical image retrieval.
Main Methods:
- Review of recent advances in computer vision, machine learning, and information retrieval.
- Analysis of the general pipeline for large-scale retrieval.
- Examination of algorithms for feature representation, indexing, and searching.
Main Results:
- Identification of key techniques for handling large-scale medical image data.
- Discussion of evaluation protocols and diverse applications in exploratory and diagnostic scenarios.
- Overview of current algorithms and their relevance to medical image analytics.
Conclusions:
- Large-scale medical image retrieval methods, leveraging AI, are crucial for effective medical image analytics.
- Future directions aim to further enhance the performance and capabilities of these retrieval systems.
