Neural Network Image Annotation for Low-Label Medical Segmentation
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Solution Overview
Problem
Accurate detection of objects in medical imaging requires large labeled datasets, which are difficult to collect, especially for applications such as medical imaging, where a skilled radiologist typically spends over 45 minutes to segment organs and tumors.
Innovation Solution
Utilization of a segmentation architecture that includes encoders and decoders, trained end-to-end using gradient-based optimization with boundary enhancement loss, and active learning techniques like Stein variational gradient descent (SVGD) to select uncertain data points for training, reducing the need for large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large labeled datasets are used for training, then detection accuracy is improved, but data collection difficulty and time increase
Solution Approach 1:
The system performs preliminary segmentation using the trained model before final detection, preparing candidate regions in advance. This preliminary action reduces the complexity of subsequent detection tasks and enables accurate results with fewer labeled examples
Solution Approach 2:
The system implements feedback loops where detection results are continuously refined through iterative processing. The model learns from its own predictions and adjusts segmentation boundaries, improving accuracy without requiring proportionally more training data
2Measurement precision
If skilled radiologists perform manual segmentation, then segmentation accuracy is improved, but time consumption increases
Solution Approach 1:
The system enables self-service segmentation where the automated model performs segmentation independently without requiring skilled radiologist intervention for each case. The model serves itself by continuously learning from training data and improving its own performance, achieving both accuracy and efficiency
Solution Approach 2:
The patent replaces the mechanical process of manual radiologist segmentation with an automated computational system. The neural network model substitutes human manual operations, maintaining segmentation quality while dramatically reducing time requirements
3Measurement precision
If boundary enhancement loss is used in training, then boundary detection precision is improved, but training complexity increases
Solution Approach 1:
The boundary enhancement loss function applies different quality requirements to different regions of the image. Boundary regions receive enhanced attention and stricter precision requirements, while interior regions use standard loss functions. This localized approach improves boundary detection without uniformly increasing complexity across the entire training process
Data Source
AI summary
Apparatuses, systems, and techniques are presented to predict annotations for objects in images. In at least one embodiment, one or more annotations corresponding to one or more objects within one or more images are generated based, at least in part, on one or more neural networks iteratively trained using the one or more images.