Non-Convolutional Microscopy Imaging for Faster Virtual Staining
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Solution Overview
Problem
Existing image processing methods in neural networks, particularly for virtual staining in microscopy, rely on complex convolution operations that consume significant computation time and energy.
Innovation Solution
A non-convolutional neural network architecture using element-wise sum operations, similarity metrics, and paths with contracting and expanding layers to process images without convolution, employing techniques like bilinear interpolation and skip/residual connections to enhance image resolution and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks are used for virtual staining in microscopy, then image processing quality is improved, but computation time and energy consumption increase significantly
Solution Approach 1:
The patent extracts and removes the convolution operation from the neural network architecture. Instead of using convolutional layers, the invention employs fully connected layers with element-wise operations, thereby eliminating the computationally expensive convolution step while maintaining image processing functionality for virtual staining
Solution Approach 2:
The patent changes the fundamental operational parameters of the neural network by replacing convolution operations (which involve sliding window multiplications and summations) with element-wise operations and matrix multiplications. This parameter change in the computational approach significantly reduces computation time while achieving comparable virtual staining results
2Measurement precision
If convolutional neural networks are used for virtual staining in microscopy, then image processing quality is improved, but energy consumption increases significantly
Solution Approach 1:
The patent extracts and removes the convolution operation from the neural network architecture. Instead of using convolutional layers, the invention employs fully connected layers with element-wise operations, thereby eliminating the computationally expensive convolution step while maintaining image processing functionality for virtual staining
Solution Approach 2:
The patent substitutes the mechanical convolution operation (sliding window with multiplication and summation) with a different computational mechanism based on element-wise operations and matrix multiplications. This substitution reduces the computational burden and associated energy consumption while achieving the same virtual staining objective
3Measurement precision
If complex convolution operations are used in neural networks, then image enhancement capability is improved, but device complexity increases
Solution Approach 1:
The patent extracts and removes the convolution operation from the neural network architecture. Instead of using convolutional layers, the invention employs fully connected layers with element-wise operations, thereby eliminating the computationally expensive convolution step while maintaining image processing functionality for virtual staining
Solution Approach 2:
The patent changes the fundamental operational parameters of the neural network by replacing convolution operations (which involve sliding window multiplications and summations) with element-wise operations and matrix multiplications. This parameter change in the computational approach significantly reduces computation time while achieving comparable virtual staining results
Data Source
AI summary
A method, device, and computer program product are designed for non-convolutional image processing in microscopy of an input image into an output image using an artificial neural network with at least one contracting path including layers, at least one expanding path including layers, and at least one filter kernel. The method includes determining, in one or multiple artificial neural network layers, a similarity metric between at least one filter kernel and one output of the previous layer. Additionally, in at least one layer of the contracting path, the resolution of the output of the previous layer is reduced, and, in at least one layer of the expanding path, the resolution of the output of the previous layer is increased. The first artificial neural network layer treats the input image as the output of the previous layer, and the output of the last artificial neural network layer is the output image.