Deep Supervision Module for Medical Image Segmentation Precision
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Solution Overview
Problem
Medical image diagnosis relies heavily on human expertise, leading to variable accuracy and high misdiagnosis rates due to fatigue and complexity of medical-image data, especially in large-scale data processing.
Innovation Solution
An image processing method utilizing a deep-learning algorithm with a deep-supervision approach, introducing a hidden-layer-assisted loss function to optimize image segmentation models, improving feature quality and reducing segmentation errors without increasing inference time or computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation models are used, then the model structure is simple, but the segmentation precision is low
Solution Approach 1:
The patent embeds a deep supervision module within the existing UNet architecture, creating a nested structure where the deep supervision mechanism is integrated into the encoder-decoder framework. This allows the model to maintain its original simple structure while incorporating advanced multi-scale feature extraction capabilities that improve segmentation precision without fundamentally redesigning the entire system.
Solution Approach 2:
The patent introduces multi-scale feature extraction by processing images at different scales and depths simultaneously. The deep supervision mechanism operates at multiple hierarchical levels, extracting features from both shallow and deep layers, thereby adding a dimensional aspect to feature extraction that enhances segmentation precision without significantly increasing structural complexity.
2Reliability
If deep learning models with multiple layers are used, then the feature extraction capability is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction at multiple scales during the training phase by pre-processing images at different resolutions and extracting features at various hierarchical levels. This preliminary action allows the model to capture multi-scale information in advance, reducing the computational burden during inference while maintaining high recognition accuracy.
Solution Approach 2:
The patent merges features from multiple scales and depths by integrating shallow and deep features through the deep supervision mechanism. By combining these features in a unified framework, the model achieves comprehensive feature representation that improves reliability while avoiding the need for separate processing pipelines that would increase computational complexity.
3Productivity
If manual interpretation by doctors is used, then the diagnostic accuracy can be maintained, but the processing efficiency decreases
Solution Approach 1:
The patent enables the image segmentation system to perform automatic diagnostic assistance by processing medical images independently without requiring continuous human intervention. The deep learning model with deep supervision automatically extracts features, segments images, and generates diagnostic recommendations, allowing the system to serve itself in processing large volumes of medical images while maintaining consistent accuracy.
Solution Approach 2:
The patent incorporates a feedback mechanism where the deep supervision module continuously monitors and adjusts feature extraction based on segmentation results. This feedback loop allows the system to learn from its own performance and improve diagnostic accuracy over time, gradually approaching the level of expert doctors while significantly increasing processing efficiency through automation.
Data Source
AI summary
An image processing method and apparatus and a storage medium, wherein the method particularly includes firstly acquiring an image-to-be-trained sample and a label segmentation image corresponding to the image-to-be-trained sample; inputting the image-to-be-trained sample into an image segmentation model to be trained, obtaining a first image feature of a last one output layer in the image segmentation model and a second image feature of a second last output layer when the image-to-be-trained sample is being extracted by using the image segmentation model, outputting the corresponding segmented-image samples; based on the label segmentation image and the segmented-image samples, calculating the model loss function, optimizing the model parameter, and generating the image segmentation model that has been optimized; and inputting an acquired image to be processed into the image segmentation model that has been optimized, and generating segmented images corresponding to the image to be processed.