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Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data.

Code ↔ Paper

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  1. [1] § Experimental results › Experimental setup and evaluation metrics ↔ train.py, lines 9–47 · score 0.55 · weight decay, CUDA, validation, epoch, metrics, PyTorch

Paper

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The authors' code

Python · 50 lines · 2.3 KB · CC-BY-4.0 · 1 match

  1. import argparse, os, torch
  2. from pathlib import Path
  3. from lora_moe_hgd.utils import seed_all, load_yaml, save_checkpoint, to_device
  4. from lora_moe_hgd.datasets import build_loaders
  5. from lora_moe_hgd.model.seg_model import SegModel
  6. from lora_moe_hgd.engine import train_one_epoch, validate
  7. from lora_moe_hgd.schedule import curriculum_stage
  9. def main():
  10. ap = argparse.ArgumentParser()
  11. ap.add_argument('--config', type=str, default='configs/brats2018.yaml')
  12. ap.add_argument('--epochs', type=int, default=None)
  13. ap.add_argument('--num-workers', type=int, default=2)
  14. args = ap.parse_args()
  16. cfg = load_yaml(args.config)
  17. if args.epochs is not None:
  18. cfg['train']['epochs'] = int(args.epochs)
  20. device = 'cuda' if torch.cuda.is_available() else 'cpu'
  21. seed_all(42)
  23. model = SegModel(in_ch=len(cfg['data']['modalities']),
  24. embed_dim=cfg['model']['embed_dim'],
  25. depth=tuple(cfg['model']['depth']),
  26. num_heads=tuple(cfg['model']['num_heads']),
  27. lora_r=cfg['model']['lora_r'],
  28. num_experts=cfg['model']['moe']['num_experts'],
  29. top_k=cfg['model']['moe']['top_k'],
  30. out_ch=cfg['model']['out_channels']).to(device)
  32. opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
  33. lr=cfg['train']['lr'], weight_decay=cfg['train']['weight_decay'])
  35. milestones = cfg['train']['curriculum_epochs']
  36. best = {'WT':0,'TC':0,'ET':0,'mean':0}
  37. for epoch in range(cfg['train']['epochs']):
  38. stage = curriculum_stage(epoch, milestones) # 0,1,2
  39. tr_loader, va_loader = build_loaders(cfg, stage_idx=stage, num_workers=args.num_workers)
  40. loss, sec = train_one_epoch(model, tr_loader, opt, device, stage)
  41. metrics = validate(model, va_loader, device)
  42. mean_d = (metrics['WT']+metrics['TC']+metrics['ET'])/3.0
  43. if mean_d > best['mean']:
  44. best = {'WT':metrics['WT'],'TC':metrics['TC'],'ET':metrics['ET'],'mean':mean_d}
  45. save_checkpoint("checkpoints/best.pt", model, opt, epoch, best)
  46. print(f"Epoch {epoch:03d} | stage={stage+1} | loss={loss:.4f} | WT={metrics['WT']:.3f} TC={metrics['TC']:.3f} ET={metrics['ET']:.3f} | best={best['mean']:.3f}")
  47. print('Done. Best:', best)
  49. if __name__ == "__main__":
  50. main()

train.py, under CC-BY-4.0 · at the source

Overview

Authors: Abdullah G M Almansour1, Faisal Alshomrani2,3
  1. Radiology and Medical Imaging Department, Prince Sattam Bin Abdulaziz University, 11942 Al-Kharj, Saudi Arabia
  2. Department of Diagnostic Radiology Technology, Taibah University, 41477 Madinah, Saudi Arabia
  3. Health and Life Research Center, Taibah University, Madinah, Saudi Arabia
Institutions: Prince Sattam Bin Abdulaziz University (Saudi Arabia); Taibah University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 16921
Dates: received 20 January 2026; accepted 7 April 2026; published online 11 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-48187-x · PMID 41965874 · PMCID PMC13230987 · OpenAlex W7153549719
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), methods / tools (subfield)
Methods: Connectivity, Machine learning
Keywords: Brain tumor segmentation, Missing modality MRI, Mixture-of-experts, Low-rank adaptation, Cross-modality consistency, Uncertainty-aware decoder, Cancer, Computational biology and bioinformatics, Medical research, Oncology
MeSH: Brain Neoplasms*, Image Interpretation, Computer-Assisted*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Algorithms, Humans (* major topic)
Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Taibah University (1110-15-447)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 18331077

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (15 files), NumPy (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
19 files
At the source: doi.org/10.5281/zenodo.18331077

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-48187-x.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 10 keywords, 6 MeSH terms, 1 funder, 12 references.

Cite

This paper

Almansour, A. G. M., & Alshomrani, F. (2026). Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data. Scientific reports, 16(1), 16921. https://doi.org/10.1038/s41598-026-48187-x

BibTeX

@article{almansour2026dynamic,
author = {Almansour, Abdullah G M and Alshomrani, Faisal},
title = {{Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {16921},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-48187-x},
url = {https://doi.org/10.1038/s41598-026-48187-x},
pmid = {41965874},
pmcid = {PMC13230987}
}

RIS

TY - JOUR
AU - Almansour, Abdullah G M
AU - Alshomrani, Faisal
TI - Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/11
VL - 16
IS - 1
SP - 16921
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-48187-x
UR - https://doi.org/10.1038/s41598-026-48187-x
LA - en
ER -

CSL-JSON

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