FusionSegNet: A Hierarchical Multi-Axis Attention and gated feature fusion network for breast lesion segmentation with uncertainty modeling in ultrasound imaging
Bibliographic record
Abstract
Lesion segmentation in breast ultrasound images (BUS) is challenging due to noise, low contrast appearance, ambiguous boundaries, texture inconsistencies, and inherent uncertainty in lesion appearance. These challenges are further exacerbated by the semantic gap between encoder and decoder features in U-Net-based models. In this paper, we introduce FusionSegNet, a novel lesion segmentation network that integrates several key innovations to address these challenges. First, we propose a Fuzzy Logic-Based Multi-Scale Contextual Network as the encoder to handle noisy and uncertain areas through multi-scale attention and fuzzy membership-based uncertainty estimation. Second, we design a Weighted Multiplicative Fusion Module to effectively merge multi-scale features while suppressing noise. Third, we integrate Hierarchical Multi-Axis Attention in both the encoder and decoder to enhance focus across multiple dimensions, enabling FusionSegNet to better segment targets with varypositions, scalesscalessizesd sizes. Fourth, we introduce a Gated Multi-Scale Feature Aggregation Module that bridges both local and global information for better semantic understanding, and the newly integrated Atrous Attention Fusion Module further refines multi-scale long-range contextual details using different dilation rates. Finally, we design a Gated Multi-Scale Fusion Block which facilitates feature fusion between the encoder and decoder to maintain spatial consistency. Extensive experiments and a comprehensive ablation study on two benchmark BUS datasets validate the superiority of FusionSegNet and its integrated design choices over state-of-the-art methods. FusionSegNet achieves an mDSC of 93.22% on the UDIAT dataset and an mIoU of 80.10% on the BUSI dataset, establishing a new benchmark for lesion segmentation in BUS images. Our code can be found at https://github.com/rayhan-ahmed91/FusionSegNet .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".