Shared Encoder Regularization for Small-Data Image Segmentation
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Solution Overview
Problem
Existing machine learning models struggle to generate high-quality segmentation masks for complex images or datasets with limited training data, particularly in medical imaging, leading to poor prediction of abnormal tissue regions.
Innovation Solution
Incorporating an autoencoder error based on image reconstruction into the training process of segmentation models, using a variable autoencoder (VAE) to generate a probabilistic representation of images, which helps regularize the model and improve generalization by reducing overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing machine learning models are used for segmentation, then the model structure is simple and training is fast, but the segmentation mask quality is poor for complex images or limited training data
Solution Approach 1:
The patent combines the segmentation model with an autoencoder model into a unified architecture. The encoder shares weights between both the segmentation pathway and the autoencoder pathway, while the segmentation head and autoencoder decoder remain separate. This merging allows the model to simultaneously perform segmentation and image reconstruction, improving segmentation quality through the additional regularization provided by the reconstruction task.
Solution Approach 2:
The encoder is designed to serve dual purposes: it extracts features for segmentation mask generation and simultaneously reconstructs the input image through the autoencoder pathway. This multi-functionality allows a single component to contribute to both segmentation accuracy and regularization, reducing overfitting on limited training data while maintaining model efficiency.
2Manufacturing precision
If existing models are trained on limited training data, then training time and data collection effort are reduced, but the model generates poor quality segmentation masks
Solution Approach 1:
The autoencoder pathway provides feedback to the encoder by reconstructing the input image from the encoded features. This reconstruction feedback acts as an additional supervision signal that helps the encoder learn more robust and generalizable features, especially when training data is limited. The reconstruction error serves as a regularizer that prevents the encoder from memorizing training samples and improves generalization to unseen data.
Solution Approach 2:
The model uses the input image itself as a training target through the autoencoder reconstruction task. Instead of requiring additional labeled data, the model learns to reconstruct its own input, creating a self-supervised learning signal that regularizes the training process and improves segmentation performance on limited datasets.
3Reliability
If the model is regularized using autoencoder error, then generalization to unseen data improves, but the training process becomes more complex
Solution Approach 1:
The patent introduces a regularization term to the loss function that combines the segmentation loss and the autoencoder reconstruction loss. By adjusting the weighting parameter between these two components, the training process balances between achieving accurate segmentation and maintaining good reconstruction capability. This parameter control allows flexible tuning of the regularization strength to achieve better generalization while managing training complexity.
Data Source
AI summary
A segmentation model is trained with an image reconstruction model that shares an encoding. During application of the segmentation model, the segmentation model may use the encoding and network layers trained for the segmentation without the image reconstruction model. The image reconstruction model may include a probabilistic representation of the image that represents the image based on a probability distribution. When training the model, the encoding layers of the model use a loss function including an error term from the segmentation model and from the autoencoder model. The image reconstruction model thus regularizes the encoding layers and improves modeling results and prevents overfitting, particularly for small training sizes.