Neural Network Training with Segmented 2D and 3D Convolutional Layers
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Solution Overview
Problem
Training neural networks with high-resolution 3D images is challenging due to computational and memory usage limitations, leading to degraded performance when reducing image resolution or using small networks.
Innovation Solution
A method involving the training of a first convolutional neural network with 2D image data, followed by a second convolutional neural network that includes 3D convolutional layers, where parameters of 2D layers are fixed to minimize memory usage while maintaining performance, allowing for efficient processing of 3D image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution 3D images are used for training neural networks, then the performance and accuracy of the neural network is improved, but the computational complexity and memory usage increase significantly
Solution Approach 1:
The patent segments the neural network into two distinct parts: a first CNN trained on 2D images and a second CNN trained on 3D images. This segmentation allows the system to process high-resolution 3D images by dividing the computational task, thereby improving neural network performance while managing computational complexity through modular processing.
Solution Approach 2:
The patent applies preliminary action by first training the neural network with 2D images before proceeding to 3D image training. This preliminary training phase prepares the network with foundational features from 2D data, which reduces the computational burden during subsequent 3D training and helps manage memory usage while maintaining performance.
2Measurement precision
If high-resolution 3D images are used for training neural networks, then the performance and accuracy of the neural network is improved, but the memory usage increases significantly
Solution Approach 1:
The patent segments the training process into two phases using two separate CNNs. The first CNN processes 2D images with lower memory requirements, and the second CNN processes 3D images. This segmentation reduces peak memory usage while still enabling the network to learn from high-resolution 3D data, thus maintaining performance with reduced memory demands.
Solution Approach 2:
The preliminary training with 2D images establishes a foundation that reduces the memory burden during 3D image processing. By pre-learning features from 2D data, the network requires less memory when processing the more demanding 3D images, effectively managing memory usage while preserving performance.
3Quantity of substance
If image resolution is reduced or small networks are used to overcome memory limitations, then the memory usage is reduced, but the performance of the neural network is degraded
Solution Approach 1:
The patent uses segmentation to create a two-stage training approach where the first CNN handles 2D images and the second CNN handles 3D images. This allows the system to use smaller, more memory-efficient network architectures for each stage while collectively achieving high performance through the combination of both stages, thus overcoming memory limitations without degrading overall performance.
Data Source
AI summary
Provided is a method for training a neural network and a device thereof. The method for training a neural network with three-dimensional (3D) training image data comprising a plurality of two-dimensional (2D) training image data, comprises: training a first convolutional neural network (CNN) with the plurality of 2D training image data, wherein the first convolutional neural network comprises 2D convolutional layers; and training a second convolutional neural network with the 3D training image data, wherein the second convolutional neural network comprises the 2D convolutional layers and 3D convolutional layers configured to receive an output of the 2D convolutional layers as an input.