is-Amir/hiFEM: v0.1.0 - Initial Public Release: Physics-Based Surgical Planning
Bibliographic record
Abstract
🎉 First Public Release We are excited to release hiFEM (Hyperelastic Inverse Finite Element Method), a biomechanics-informed computational tool for patient-specific virtual surgical planning in soft tissue reconstruction. 📄 Peer-Reviewed & Published This release accompanies our paper published in Physics in Medicine & Biology (2025): DOI: 10.1088/1361-6560/adebd8 Citation: Isazadeh et al. (2025). Patient-Specific Virtual Surgical Planning for Tongue Reconstruction: Evaluating Hyperelastic Inverse FEM with Four Simulated Tongue Cancer Cases 📖 Documentation Complete function documentation in README.md Example notebook: regenerate_paper_figures.ipynb reproduces all paper results Detailed methodology in the published paper 💡 Development Status This is a research tool released for scientific and clinical exploration. It maintains a simple structure for easy integration into existing workflows via git clone and direct import. 🙏 Acknowledgments This research was funded by the Alberta Cancer Foundation, Grant Number 27 601. 📧 Contact For questions, issues, or collaboration inquiries, please open an issue on GitHub or contact the authors through the paper. What's Included Core hiFEM function with hyperelastic material model Input data for four simulated tongue cancer cases (A, B, C, D) Jupyter notebook to reproduce paper figures Complete documentation and usage examples MIT License for open collaboration Note: This is an initial release focused on functionality and reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.468 | 0.451 |
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".