Feature Ensembling for Cross-Domain Optic Disc and Cup Segmentation
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Solution Overview
Problem
Deep learning models for ocular cup and disc segmentation face significant performance degradation due to domain shift caused by variations in retinal fundus images captured with different imaging devices, making it challenging to deploy these models effectively in real-world scenarios, and the process of retraining or annotating new domains is expensive and impractical.
Innovation Solution
A neural network training method using Collaborative Feature Ensembling Adaptation (CFEA) and Collaborative Adversarial Domain Adaptation (CADA) frameworks that leverage adversarial learning and self-ensembling to stabilize the learning process, incorporating multi-scale inputs, multiple discriminators, and exponential moving average of weights to adapt to domain shifts, enabling accurate segmentation of ocular structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on well-annotated datasets, then segmentation performance is improved, but performance degrades severely on datasets with different distributions (domain shift)
Solution Approach 1:
The patent introduces a domain adapter module as an intermediary component between the fixed backbone network and the target domain data. This adapter learns to transform features from the source domain to the target domain, enabling the model to adapt to different imaging devices without retraining the entire network. The adapter acts as a mediator that bridges the gap between source and target domains, resolving the contradiction between maintaining high segmentation performance and achieving cross-domain adaptability.
Solution Approach 2:
The patent segments the neural network into two independent parts: a fixed backbone network that processes general image features and a domain adapter that handles domain-specific transformations. This segmentation allows the backbone to remain stable while the adapter adapts to different domains, solving the contradiction by separating the universal segmentation function from the domain-specific adaptation function.
2Measurement precision
If the model is retrained or fine-tuned on new domains, then performance on target domain is improved, but the process is extremely expensive and impractical
Solution Approach 1:
The patent extracts the adaptation function from the entire network by isolating a domain adapter module that can be trained independently on target domain data. This extraction allows the expensive and complex full network retraining to be replaced by a simpler, more efficient adapter training process. The adapter is trained only on the domain-specific transformation task, significantly reducing computational cost and complexity while achieving target domain performance improvement.
Solution Approach 2:
Instead of retraining the entire network (excessive action), the patent trains only the domain adapter module (partial action) on target domain data. This partial retraining approach achieves sufficient adaptation to the target domain at a fraction of the computational cost, making the process practical and economically feasible for medical imaging applications.
3Measurement precision
If manual delineation is performed by human experts, then accuracy is improved, but the process introduces subjectivity and variability
Solution Approach 1:
The patent implements a fully automated segmentation system using deep learning models that perform OD and OC segmentation without human intervention. The model processes retinal fundus images automatically, extracting features and computing CDR values through neural network inference. This self-service approach eliminates human subjectivity and variability, providing consistent and objective measurements while maintaining high accuracy through the learned segmentation boundaries.
4Ease of manufacture
If automated segmentation is implemented, then cost is reduced, but accuracy is affected by domain shift from different imaging devices
Solution Approach 1:
The patent introduces dynamic adaptation capability through the domain adapter module that automatically adjusts to different imaging devices. The adapter learns domain-specific transformations from target domain data and dynamically modifies the feature representations to match the target domain distribution. This dynamic behavior allows the automated system to maintain high segmentation accuracy across different cameras and imaging devices without manual recalibration, resolving the contradiction between cost efficiency and cross-domain accuracy.
Data Source
AI summary
Embodiments of the present disclosure are directed to training a neural network for ocular cup (OC) or ocular disc (OD) detection. One such method comprises initiating training of a first network to learn detection of OC/OD regions within a labeled source sample from a source domain; sharing training weights of the first network with a second network; initiating training of the second network to learn detection of OC/OD regions within an unlabeled sample from a target domain; transferring average training weights of the second network to a third network; initiating training of the third network to learn detection of OC/OD regions within an unlabeled sample from the target domain; computing a mean square error loss between the third network and the second network for a same target sample; and adjusting training weights of the second network based on the mean square error loss computation.