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PlexusNet: A neural network architectural concept for medical image classification.

Okyaz Eminaga¹, Mahmoud Abbas², Jeanne Shen³

¹Center for Artificial Intelligence in Medicine & Imaging and Department of Urology, Stanford School of Medicine, Stanford, CA, 94305, USA; Department of Urology, Stanford School of Medicine, Stanford, CA, 94305, USA.

Computers in Biology and Medicine

|February 8, 2023

Summary

This summary is machine-generated.

PlexusNet offers a scalable deep learning model family for medical imaging, achieving comparable performance to state-of-the-art models with fewer resources and improved explainability. Its learnable data normalization enhances generalization across diverse clinical datasets.

Keywords:

Compact models Computer vision Convolutional neural networks Deep learning Diagnostics PlexusNet

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Area of Science:

Medical Imaging
Deep Learning
Computer Vision

Background:

State-of-the-art (SOTA) convolutional neural networks are widely used in medical imaging but often face challenges with computational cost, model explainability, and fixed data normalization.
High complexity and large scale of existing models may not be optimal for resource-constrained medical imaging applications.
Increasing feature maps for classification can reduce model interpretability, a critical factor in clinical settings.

Purpose of the Study:

To introduce PlexusNet, a novel, scalable deep learning model family designed for medical imaging classification tasks.
To address limitations of current models regarding computational efficiency, explainability, and data generalization.
To develop a flexible architecture adaptable to various clinical problems with optimized resource utilization.

Main Methods:

Proposed PlexusNet, a scalable model family with a block architecture regulated by network depth, width, and branching.
Implemented a learnable data normalization algorithm for improved data generalization.
Utilized neural architecture search to tailor PlexusNet for five distinct clinical classification problems.

Main Results:

PlexusNet achieved performance non-inferior to SOTA models like ResNet-18 and EfficientNet B0/1 on five clinical classification tasks.
Demonstrated significantly lower parameter capacity and feature map requirements (ten-fold range) compared to SOTA models with similar performance.
Visualization of PlexusNet's latent features revealed distinguishable clusters, indicating enhanced model explainability.

Conclusions:

PlexusNet provides an efficient and scalable alternative to current deep learning models in medical imaging.
The model's design enhances generalization and explainability while maintaining competitive performance.
PlexusNet offers a promising solution for resource-efficient and interpretable medical image analysis.