What to Preserve, Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation
Proposes a depth-constrained adaptation framework, analyzing encoder-decoder regions' impact on forgetting, with key focus on shallow encoder and deep decoder layers.
Key Findings
Methodology
Using block-wise ablation and depth-restricted parameter updates, the study investigates how different encoder-decoder layers influence performance and forgetting. The U-Net architecture is partitioned into depth-specific blocks, and performance drops are measured after ablating each block. Controlled experiments restrict updates to certain regions, revealing the importance of shallow encoder and deep decoder layers. Experiments on three public gynecological datasets (MRI, PET/CT) demonstrate that limiting updates near the bottleneck minimizes forgetting (average 0.083), while extending updates to shallow layers increases forgetting (average 0.643). The approach combines ablation analysis with selective parameter freezing, providing insights into the hierarchical contribution of network regions during continual learning.
Key Results
- Shallow encoder layers (E0-E2) and late decoder layers (D0-D2) are critical, with ablation causing performance drops δb≥0.99, indicating their dominant role. Deep encoder layers and bottleneck contribute less, with minimal performance impact when ablated. Depth-restricted updates at bottleneck regions result in low forgetting (mean 0.083), whereas expanding to shallow encoder layers causes significant forgetting (mean 0.643). The method surpasses traditional regularization and replay strategies, offering a practical solution without storing old data.
- Experiments across three datasets show that performance heavily depends on specific network regions. Limiting updates to bottleneck-adjacent layers preserves knowledge effectively, while broader updates lead to increased forgetting. The findings highlight the importance of hierarchical analysis in designing continual learning algorithms for medical imaging, especially under large distribution shifts.
Significance
This work advances understanding of how hierarchical encoder-decoder structures influence forgetting during continual learning in medical image segmentation. By identifying key layers whose updates cause minimal forgetting, it guides the development of more robust models capable of learning across diverse, multi-modal datasets without catastrophic forgetting. The approach addresses practical constraints such as data privacy, as it avoids storing previous datasets, making it highly relevant for clinical deployment. The insights gained can inform future architectures and training strategies, ultimately improving the reliability and adaptability of AI systems in healthcare.
Technical Contribution
The study introduces a novel depth-constrained adaptation framework that combines block-wise ablation with selective parameter freezing. This allows precise control over which network regions are updated during sequential learning, reducing forgetting while maintaining performance. The approach departs from traditional regularization or replay-based methods, offering a data-efficient, architecture-aware solution. Theoretically, it demonstrates that shallow encoder and deep decoder layers are pivotal for information retention, providing a new perspective on hierarchical robustness in encoder-decoder networks. The framework can be extended to other architectures and tasks beyond medical imaging.
Novelty
This is the first systematic analysis of how different depths within encoder-decoder networks influence forgetting in continual learning. The depth-constrained adaptation strategy, focusing on selectively updating bottleneck-adjacent layers, is a novel approach that significantly reduces forgetting without requiring old data. Unlike prior work that treats all layers equally, this study emphasizes the hierarchical importance of network regions, offering a new paradigm for designing robust continual learning models in complex, heterogeneous environments such as medical imaging.
Limitations
- The framework is primarily validated on U-Net based architectures; its effectiveness on other network types remains to be tested. Extreme distribution shifts may still challenge the approach, requiring further adaptation mechanisms.
- The experiments rely on publicly available datasets, which may not fully capture the complexity of real-world clinical data. Additional validation in clinical settings is necessary.
- While avoiding data storage, the method assumes access to the network architecture and the ability to freeze specific layers, which may not always be feasible in all deployment scenarios.
Future Work
Future research will explore adaptive region selection mechanisms that dynamically identify critical layers during training. Extending the framework to multi-task and multi-modal settings, incorporating self-supervised signals, and testing on larger, more diverse clinical datasets will enhance robustness. Combining depth-constrained updates with other continual learning strategies, such as meta-learning or unsupervised domain adaptation, could further improve performance under severe distribution shifts. Additionally, developing automated tools for layer importance estimation will facilitate broader clinical adoption.
AI Executive Summary
This study addresses a fundamental challenge in medical image segmentation: how to enable models to learn continuously across diverse datasets without forgetting previously acquired knowledge. Traditional approaches like regularization and data replay often fall short in clinical settings due to privacy constraints and large distribution shifts. To tackle this, the authors propose a depth-constrained adaptation framework that selectively updates specific regions of encoder-decoder networks, focusing on shallow encoder and deep decoder layers. By systematically analyzing the hierarchical contribution of network regions through block-wise ablation, the research uncovers that these layers are critical for performance retention during continual learning.
Experimental validation on three public gynecological datasets—MRI and PET/CT—demonstrates that restricting parameter updates to bottleneck-adjacent regions results in minimal forgetting (average 0.083 Dice score degradation). Extending updates to shallower encoder layers causes a sharp increase in forgetting (average 0.643), confirming the importance of update location. This approach surpasses conventional regularization and replay methods, providing a practical, data-efficient solution that aligns with clinical privacy requirements.
The findings have broad implications for designing robust AI systems capable of lifelong learning in healthcare. By emphasizing hierarchical network analysis, the work guides future architecture design and training strategies, fostering models that adapt seamlessly to evolving clinical data. Despite its strengths, the method's applicability to other architectures and extreme distribution shifts warrants further exploration. Overall, this research marks a significant step toward reliable, privacy-preserving continual learning in medical imaging, with promising avenues for future development.
Deep Dive
Abstract
Medical image segmentation models are typically trained under the assumption that all data are available simultaneously. However, in clinical practice, datasets often arrive sequentially, requiring models to adapt continuously to evolving data distributions. We study this problem in gynecological image segmentation, where substantial heterogeneity across imaging modalities, anatomical structures, and annotation protocols creates a particularly challenging continual learning setting. Under these large distribution shifts, existing continual learning methods struggle to preserve previously learned knowledge, leading to catastrophic forgetting. To better understand forgetting in this setting, we investigate how different encoder--decoder regions influence segmentation performance and forgetting during continual gynecological segmentation. Through block-wise ablation analysis, we observe that ablating early encoder and late decoder regions results in the largest performance degradation, indicating that segmentation performance depends unevenly across the network hierarchy. Using controlled adaptation experiments, we further show that forgetting remains limited when updates are restricted to bottleneck-adjacent regions, but increases sharply once shallower encoders and decoders become trainable, even when only a small subset of parameters is updated. These findings suggest that forgetting in the encoder-decoder architecture is strongly influenced by where updates occur across network depth during continual learning. Full code and analysis pipelines will be made publicly available upon acceptance.