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Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methodology › Benchmark dataset ↔ mtugan/dataset.py, lines 9–105 · score 0.73 · horizontal flipping, brightness, elastic, nearest, resized, augmentation
  2. [2] § Methodology › Model architecture › Loss functions › Generator loss ↔ mtugan/losses.py, lines 25–30 · score 0.69 · focal loss, segmentation loss, Dice loss, generator loss, weighted, class
  3. [3] § Methodology › Model architecture › Loss functions › Discriminator loss ↔ mtugan/losses.py, lines 32–38 · score 0.66 · gradient penalty, discriminator loss, Dice loss, fake
  4. [4] § Methodology › Evaluation metrics ↔ main.py, lines 30–98 · score 0.56 · class metrics, F1 score, hippocampal subregions, Precision, Recall, CA1
  5. [5] § Methodology › Model architecture › The generator ↔ mtugan/gan.py, lines 7–66 · score 0.56 · LeakyReLU, padding, Max, stride, blocks, bottleneck
  6. [6] § Methodology › Training protocol ↔ mtugan/train.py, lines 34–119 · score 0.51 · Adam, warm, phase, optimizers, trained, discriminator

Paper

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The authors' code

Python · 39 lines · 1.9 KB · MIT · 2 matches

  1. import torch
  2. import torch.nn as nn
  3. import torch.nn.functional as F
  5. from mtugan.utils import gradient_penalty
  8. # Loss Functions
  9. def dice_loss(y_true, y_pred, epsilon=1e-8):
  10. numerator = 2 * torch.sum(y_true * y_pred, dim=(1, 2, 3))
  11. denominator = torch.sum(y_true + y_pred, dim=(1, 2, 3)) + epsilon
  12. return 1 - (numerator / denominator).mean()
  14. def focal_loss(y_pred, y_true, gamma=2.0, alpha=None, reduction='mean'):
  15. log_pt = F.log_softmax(y_pred, dim=1)
  16. pt = torch.exp(log_pt)
  17. y_true_onehot = F.one_hot(y_true, num_classes=3).float()
  18. if alpha is not None:
  19. focal_weight = alpha[y_true] * (1 - pt[range(len(y_true)), y_true]) ** gamma
  20. else:
  21. focal_weight = (1 - pt[range(len(y_true)), y_true]) ** gamma
  22. loss = -focal_weight * log_pt[range(len(y_true)), y_true]
  23. return loss.mean() if reduction == 'mean' else loss.sum()
  25. def generator_loss(y_true_seg, y_pred_seg, fake_output, y_true_cls, y_pred_cls, class_weights,
  26. lambda_dice=2.0, lambda_adv=0.1, lambda_cls=0.3): # Adjusted to prioritize segmentation
  27. seg_loss = dice_loss(y_true_seg, y_pred_seg)
  28. adv_loss = nn.BCELoss()(fake_output, torch.ones_like(fake_output))
  29. cls_loss = focal_loss(y_pred_cls, y_true_cls, alpha=class_weights)
  30. return lambda_dice * seg_loss + lambda_adv * adv_loss + lambda_cls * cls_loss
  32. def discriminator_loss(discriminator, real_output, fake_output, ground_truth, lambda_dice=0.5, lambda_bce=0.5, lambda_gp=1.0, device='cuda'):
  33. dice_real = dice_loss(ground_truth, real_output)
  34. bce_real = nn.BCELoss()(real_output, torch.ones_like(real_output))
  35. dice_fake = dice_loss(ground_truth, fake_output)
  36. bce_fake = nn.BCELoss()(fake_output, torch.zeros_like(fake_output))
  37. gp = gradient_penalty(discriminator, ground_truth, fake_output.detach(), device)
  38. return lambda_dice * (dice_real + dice_fake) / 2 + lambda_bce * (bce_real + bce_fake) / 2 + lambda_gp * gp

losses.py at commit bc6784d, under MIT · at the source

Overview

Authors: Sayed Mehedi Azim1, Renuka Kumar2, Brian Corbett1,2, Iman Dehzangi1,3,4
  1. Center for Computational and Integrative Biology, Rutgers University,Camden, NJ 08103 USA
  2. Department of Biology, Rutgers University,Camden, NJ 08103 USA
  3. Department of Computer Science, Rutgers University,Camden, NJ 08103 USA
  4. Rutgers Cancer Institute, Rutgers University,New Brunswick, NJ 08901 USA
Institutions: Rutgers, The State University of New Jersey (United States)
Journal: Scientific reports, volume 16, issue 1, article 24430
Dates: received 11 November 2025; accepted 21 April 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-50475-5 · PMID 42209557 · PMCID PMC13448715 · OpenAlex W7162671199
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning
Keywords: Multi-task learning, Generative adversarial networks, Hippocampal region segmentation, Subregion classification, Computational biology and bioinformatics, Neuroscience
MeSH: Hippocampus*, Image Processing, Computer-Assisted*, Animals, Generative Adversarial Networks, Generative Artificial Intelligence, Glutamate Decarboxylase, Mice, Proto-Oncogene Proteins c-fos (* major topic)
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NRT-NSF (2152059)
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 6 matches between paragraphs and lines of code.

MLBC-lab/MT-UGAN

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bc6784dc81deabf722a4a2416c3decd040511e82, 3 October 2025
Languages: Python (8)
Size: 12 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Introduction”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (7 files), NumPy (3 files), OpenCV (2 files), scikit-learn (2 files), Matplotlib (1 file), Pillow (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/MLBC-lab/MT-UGAN

The paper's code and data availability statement is in the Data section.

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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;
  • 8 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Code and 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/s41598-026-50475-5.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 8 MeSH terms, 1 funder, 25 references.

Cite

This paper

Azim, S. M., Kumar, R., Corbett, B., & Dehzangi, I. (2026). Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks. Scientific reports, 16(1), 24430. https://doi.org/10.1038/s41598-026-50475-5

BibTeX

@article{azim2026segmentation,
author = {Azim, Sayed Mehedi and Kumar, Renuka and Corbett, Brian and Dehzangi, Iman},
title = {{Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {24430},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50475-5},
url = {https://doi.org/10.1038/s41598-026-50475-5},
pmid = {42209557},
pmcid = {PMC13448715}
}

RIS

TY - JOUR
AU - Azim, Sayed Mehedi
AU - Kumar, Renuka
AU - Corbett, Brian
AU - Dehzangi, Iman
TI - Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/28
VL - 16
IS - 1
SP - 24430
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50475-5
UR - https://doi.org/10.1038/s41598-026-50475-5
LA - en
ER -

CSL-JSON

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