A lightweight hierarchical feature fusion network for surgical instrument segmentation in internet of medical things
Bibliographic record
Abstract
Minimally invasive surgeries (MIS) enhance patient outcomes but pose challenges such as limited visibility, complex hand-eye coordination, and manual endoscope control. The rise of the Internet of Medical Things (IoMT) and telesurgery further demands efficient and lightweight solutions. To address these limitations, we propose a novel lightweight hierarchical feature fusion network (LHFF-Net) for surgical instrument segmentation. LHFF-Net integrates high-, mid-, and low-level encoder features through three novel modules: the multiscale feature aggregation (MFA) module which can capture fine-grained and coarse features across scales, the enhanced spatial attention (ESA) module, prioritizing critical spatial regions, and the enhanced edge module (EEM), refining boundary delineation. The proposed model was evaluated on two benchmark datasets, Kvasir-Instrument and UW-Sinus-Surgery, achieving mean Dice coefficients (mDC) of 97.87% and 88.83%, respectively, along with mean intersection over union (mIOU) scores of 95.87% and 84.33%. These results highlight LHFF-Net’s ability to deliver high segmentation accuracy while maintaining computational efficiency with only 2.2 million parameters. This combination of performance and efficiency makes LHFF-Net a robust solution for IoMT applications, enabling real-time telesurgery and driving innovations in healthcare.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".