Feature Reconfiguration With Visual Prior for Medical Lesion Segmentation
FreNet introduces a novel feature reconfiguration framework for medical lesion segmentation that operates at both pixel-level (before encoding) and feature-level (during encoding) The Implicit Prior Neural Network (IPNN) leverages visual priors from SAM to model continuous spatial fields and suppress background interference before the encoding stage The Dual-domain Feature Reconfiguration (DFR) module combines Frequency Decoupling (FDM) and Spatial Localization (SLM) to handle diverse lesion mor
Analysis
TL;DR
- FreNet introduces a novel feature reconfiguration framework for medical lesion segmentation that operates at both pixel-level (before encoding) and feature-level (during encoding)
- The Implicit Prior Neural Network (IPNN) leverages visual priors from SAM to model continuous spatial fields and suppress background interference before the encoding stage
- The Dual-domain Feature Reconfiguration (DFR) module combines Frequency Decoupling (FDM) and Spatial Localization (SLM) to handle diverse lesion morphology during encoding
- Extensive experiments across 9 medical image segmentation benchmarks and 3 imaging modalities demonstrate significant improvements over state-of-the-art methods
- On the challenging ETIS dataset, FreNet achieves Dice improvements of 5.0% over SOTA and 7.2% over SAM
Why It Matters
This work addresses two fundamental challenges in medical image segmentation—complex background interference and diverse lesion morphology—by introducing early prior guidance and progressive feature reconfiguration, which existing encoder-decoder architectures largely overlook. For AI practitioners working in medical imaging, FreNet demonstrates how leveraging large foundation model priors (SAM) can significantly boost performance on specialized clinical tasks without requiring massive task-specific training data.
Technical Details
- Implicit Prior Neural Network (IPNN): Models a continuous spatial field using visual priors extracted from SAM, performing pixel-level reconfiguration on input images before they enter the main encoder, effectively suppressing background noise
- Dual-domain Feature Reconfiguration (DFR): A module that progressively reconfigures backbone features during encoding, combining frequency-domain and spatial-domain operations for robust lesion representation
- Frequency Decoupling Module (FDM): Decouples backbone features in the frequency domain to enhance discriminability between foreground lesions and complex backgrounds
- Spatial Localization Module (SLM): Relocates features spatially after frequency decoupling to improve spatial stability and preserve structural integrity of lesion boundaries
- Evaluation: Tested on 9 medical image segmentation benchmarks across 3 imaging modalities, with notable results on the ETIS dataset showing 5.0% Dice improvement over SOTA and 7.2% over SAM
Industry Insight
- The integration of foundation model priors (SAM) into specialized medical imaging pipelines represents a scalable strategy for improving segmentation accuracy without extensive labeled data collection
- The dual-domain (frequency + spatial) feature reconfiguration approach could be generalized beyond medical imaging to other domains requiring robust foreground-background separation under high visual complexity
- The emphasis on early-stage prior guidance suggests that incorporating external knowledge before deep encoding may yield diminishing returns compared to post-hoc refinement, influencing future architecture design priorities in medical AI
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