Semantic AutoSAM: Self-Prompting Segment Anything Model for Semantic Segmentation of Medical Images
Bibliographic record
Abstract
Segment Anything Model (SAM) is a foundation model that can be prompted with sparse prompts, like boxes or points, and dense prompts such as masks. SAM outputs binary masks based on the given prompts but lacks semantic understanding as it doesn't output the class of the predicted mask. We propose Semantic AutoSAM, a semantic segmentation model that builds upon SAM's binary segmentation. Semantic AutoSAM replaces SAM's manual prompt encoder with a lightweight cross-attention module, enabling it to predict prompt embeddings directly from the image features. This eliminates the need for manual prompting.In our experiments on the FLAIR 2022 dataset (20 CT scans) and a hip ultrasound dataset (4849 2D images), Semantic AutoSAM matches the performance of using groundtruth bounding box prompts for most organs. Our proposed method achieves a Dice score of 0.62 in the FLAIR dataset, and MobileSAM with groundtruth box achieves 0.7. In the hip ultrasound dataset, our approach achieves a Dice score of 0.83, surpassing MobileSAM's slightly lower score of 0.81 despite MobileSAM having access to the groundtruth box for prediction. Notably, our method doesn't require manual prompts at test time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".