Histopathology Image Classification With Global-Local Feature Fusion
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Solution Overview
Problem
Traditional convolutional neural networks struggle to accurately classify histopathological images due to their inability to effectively capture and integrate localized pattern variations, leading to suboptimal classification accuracy in texture-rich domains like histopathology.
Innovation Solution
An extended deep-learning network is developed, integrating a pretrained dense convolutional neural network with trainable residual layers that extract both global and local features, using a skip connection with a 1×1 kernel and sigmoid activation function to concatenate spatial and spectral features for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional convolutional neural networks are used for histopathological image classification, then the system structure is simple and easy to implement, but the classification accuracy is insufficient due to inability to capture fine-grained local texture patterns
Solution Approach 1:
The network is segmented into distinct functional modules: a pretrained dense convolutional neural network for global feature extraction, trainable residual layers for local feature extraction, and a concatenation layer for feature integration. This segmentation allows each module to specialize in specific feature types, improving overall classification accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent merges global spatial features extracted by the pretrained dense convolutional network with local spectral features extracted by the trainable residual layers through a concatenation operation. This combination of multiple feature types creates a more comprehensive feature representation that captures both overall structural patterns and fine-grained texture details, thereby improving classification accuracy.
2Loss of information
If only global convolutional features are extracted, then the network architecture remains simple, but local spectral features critical for texture discrimination are lost
Solution Approach 1:
The patent extracts local spectral features separately from global spatial features using dedicated trainable residual layers with skip connections. This extraction mechanism specifically targets local texture patterns and spectral variations that are critical for histopathological classification, preventing information loss while adding targeted complexity only where needed for local feature capture.
Solution Approach 2:
The network applies local quality by using trainable residual layers with 1×1 kernels specifically designed to capture local spectral variations in histopathological textures. These layers focus computational resources on extracting fine-grained local features rather than applying uniform processing across the entire image, thereby preserving critical local information with efficient use of network complexity.
3Reliability
If pretrained convolutional layers are used alone, then training efficiency is high, but the network underperforms in texture-rich domains due to lack of local pattern capture mechanisms
Solution Approach 1:
The patent uses a pretrained dense convolutional neural network as the base architecture, leveraging pre-trained weights for efficient global feature extraction. This preliminary action provides a strong foundation that converges quickly during training, while additional trainable residual layers are then added to capture local texture patterns specific to histopathological images, improving reliability in texture-rich domains without sacrificing training efficiency.
Data Source
AI summary
A method and a system of diagnostic assistance. The method includes obtaining a plurality of histopathological images of tissue samples. The method includes receiving, by a processing circuitry configured with a pretrained dense convolutional neural network, a histopathological image of the plurality of histopathological images and extracting global features of the histopathological image. The global features are spatial features of the histopathological image. The method includes extracting, by the processing circuitry, local features of the histopathological image. The local features are spectral features. The method includes concatenating, by a concatenation operator, the global features with the local features to obtain concatenated features. The method includes outputting, by a classification layer, a classification of the histopathological image based on the concatenated features.