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3D Deep Learning on Medical Images: A Review.

Satya P Singh^1,2, Lipo Wang³, Sukrit Gupta⁴

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore.

Sensors (Basel, Switzerland)

|September 10, 2020

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This summary is machine-generated.

This paper reviews three-dimensional convolutional neural networks (3D CNNs) for medical image analysis. It covers their history, mathematical basis, preprocessing, applications, and future trends in deep learning for healthcare.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning models, particularly convolutional neural networks (CNNs), are increasingly used in medical imaging for disease diagnosis.
Three-dimensional CNNs (3D CNNs) have emerged as a powerful tool for analyzing complex medical image data.

Purpose of the Study:

To trace the historical development of 3D CNNs from their machine learning origins.
To provide a mathematical description and necessary preprocessing steps for 3D CNNs in medical imaging.
To review significant research utilizing 3D CNNs for medical image analysis tasks like classification, segmentation, detection, and localization.

Main Methods:

Historical review of 3D CNN development.
Mathematical formulation of 3D CNNs.

Keywords:

3D convolutional neural networks 3D medical images classification detection localization segmentation

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Description of medical image preprocessing techniques for 3D CNN input.

Comprehensive literature review of 3D CNN applications in various medical domains.

Main Results:

3D CNNs have evolved significantly, building upon foundational machine learning and CNN architectures.
Various research studies demonstrate the effectiveness of 3D CNNs in diverse medical imaging applications.
Key preprocessing steps are crucial for optimizing 3D CNN performance in medical image analysis.

Conclusions:

3D CNNs represent a significant advancement in medical image analysis, enhancing diagnostic efficiency.
Challenges remain in the implementation and generalizability of deep learning models in medicine.
Future trends point towards further refinement and broader adoption of 3D CNNs and related AI techniques in healthcare.