Lesion Boundary-Aware Adaptation of Segment Anything Model for 2D Medical Image
Bibliographic record
Abstract
The Segment Anything Model (SAM), serving as a foundational vision model, has demonstrated an extraordinary capability in segmenting natural images. However, its efficacy in the domain of medical image analysis leaves much to be desired, primarily due to the irregular shapes and indistinct edges characteristic of lesions. There is a pressing need to augment SAM’s proficiency in recognizing lesion boundaries. Achieving precise segmentation of such lesions requires a blend of high-level global semantic information and low-level local boundary details. In response to this challenge, we introduce an auxiliary boundary-aware Convolutional Neural Network (CNN) module, equipped with a boundary generator, to enhance the model’s focus on boundary feature extraction. Furthermore, to leverage both the intricate low-level features in the lower layers and the high-level textural features in the deeper layers, we employ feature adapters to fuse the multi-scale features derived from the SAM encoder, thereby aggregating a wealth of enriched information. The performance superiority of our model is demonstrated through comprehensive evaluation on three different medical image segmentation tasks, and experimental results highlight the effectiveness of our proposed model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".