Neural Network Segmentation with Uncertainty Refinement
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Solution Overview
Problem
Existing automated image segmentation methods, particularly deep learning models, fail to generalize effectively when applied to image data from a shifted domain due to overfitting, leading to unreliable and irreproducible interpretations in fields like medicine and materials science.
Innovation Solution
The method involves training a neural network with dropout layers for image segmentation, where a subset of nodes are dropped out during training, and active dropout layers are used during inference to generate uncertainty values, allowing for refinement of segmentation labels based on uncertainty thresholds, thereby addressing domain shifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on labeled data from a first domain, then segmentation accuracy is improved, but the model fails to generalize when applied to image data from a shifted domain
Solution Approach 1:
The patent applies preliminary action by performing multiple inferences during training with dropout layers activated, so that the model learns to handle domain shifts proactively. The model is pre-exposed to variability through dropout during training, enabling it to generalize better to shifted domains without requiring explicit domain adaptation training.
Solution Approach 2:
The patent implements feedback by using uncertainty values generated during inference to refine segmentation labels. When uncertainty exceeds a threshold, the segmentation label is replaced with the next highest probability class, creating a feedback loop that corrects potential misclassifications and improves robustness to domain shifts.
2Productivity
If automated image segmentation is performed quickly and consistently, then productivity is improved, but reliability deteriorates due to overfitting
Solution Approach 1:
The patent applies preliminary action by performing multiple inferences during training with dropout layers activated, so that the model learns to handle domain shifts proactively. The model is pre-exposed to variability through dropout during training, enabling it to generalize better to shifted domains without requiring explicit domain adaptation training.
Solution Approach 2:
The patent implements feedback by using uncertainty values generated during inference to refine segmentation labels. When uncertainty exceeds a threshold, the segmentation label is replaced with the next highest probability class, creating a feedback loop that corrects potential misclassifications and improves robustness to domain shifts.
3Adaptability or versatility
If dropout layers are used during training, then model generalization is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using dropout layers selectively in specific layers of the neural network rather than uniformly across all layers. This allows the model to benefit from dropout's generalization effects while minimizing the computational overhead, balancing generalization improvement with computational efficiency.
Data Source
AI summary
A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. The label of any image element with an uncertainty value above a predefined threshold is replaced with a new label corresponding to the class with the next highest value.