Insights into Imaging· 2018Q1· Review
Convolutional neural networks: an overview and application in radiology
- 4,622citations
- Q1SCImago
- 2018year
Short summary
Convolutional Neural Networks (CNNs), a powerful deep learning technique dominant in computer vision, are increasingly being explored for their potential to enhance radiological tasks.
AI-generated from the title and abstract; the full text is not read.
Key points
- CNNs are deep learning models that excel at computer vision tasks by learning feature hierarchies.
- They are composed of layers like convolution, pooling, and fully connected layers.
- CNNs are being applied to various radiological tasks to improve diagnostic performance.
- Key challenges include small dataset sizes and overfitting, with techniques to mitigate these being discussed.
AI-generated from the title and abstract; the full text is not read.
Abstract
Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. KEY POINTS: • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.
The authors' abstract, as published at the source. Insights into Imaging, 2018 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine