Lightweight Grey Wolf Optimization-Driven
Hyperparameter Tuning of U-Net
for Robust Brain Tumor Segmentation
Shoffan Saifullah | Rafał Dreżewski | Anton Yudhana
26th International Conference on Computational Science
29 June - 1 July 2026 • DESY • Hamburg • Germany
ICCS 2026
Why this problem matters
U-Net works well, but its performance depends heavily on tuning.
Brain tumor MRI segmentation requires accurate masks, yet manual annotation is time-consuming and variable.
U-Net is effective, but depth, filters, kernel size, dropout, learning rate, and batch size change the result.
Grid and random search become costly; black-box optimization may hide which parameters matter.
The goal is not a heavier network; the goal is a better configuration under a constrained budget.
U-Net
+
Hyperparameters
Lightweight search
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
02
Core idea: GWO tunes a parameterized U-Net
Each wolf represents one candidate hyperparameter vector.
Candidate vector
x = [η, p, F, K, B, D]
η: learning rate
p: dropout rate
F, K, B, D: base filters, kernel, batch, depth
Fitness minimizes segmentation error:
f(x) = 1 - DSC
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
03
Lightweight optimization workflow
Short, low-resolution evaluations first; full-resolution retraining only at the end.
1
Initialize wolves
sample search space
2
Evaluate quickly
downscaled inputs
3
Update leaders
α, β, δ guide search
4
Select best
stable configuration
5
Retrain
full-resolution model
Search range
η: 1e-5 to 1e-2
p: 0.0 to 0.5
F: 16/32/48/64
K: 3 or 5
B: 4/8/16
D: 2/3/4
The search is intentionally compact: optimize only the parameters that change capacity, receptive field, regularization, and training dynamics.
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
04
Experimental setup
Two datasets, compact preprocessing, and overlap/boundary evaluation.
FBTS
T1-CE MRI slices
Meningioma • Glioma • Pituitary
Binary masks
BraTS 2021
FLAIR • T1 • T1CE • T2
WT / TC / ET annotations
Generalization test
Preprocessing
grayscale conversion
resize and min-max normalization
short optimization training, then final retraining
Metrics
DSC
JI
HD
ASSD
Training: Adam optimizer with binary cross-entropy; random seeds fixed.
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
05
Optimization behavior: fast convergence, interpretable sensitivity
The search finds a stable region rather than a single isolated optimum.
~2
iterations to jump from ≈0.26 to >0.82 Dice
≈10
evaluations to exceed 0.80 best-so-far Dice
10⁻³
learning-rate region with strongest performance
D=3
moderate depth provides stable performance
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
06
Segmentation performance
High FBTS cross-validation scores and strong BraTS 2021 whole-tumor generalization.
Best reported values
FBTS DSC 0.9820
FBTS JI 0.9652
BraTS WT DSC 0.9778
BraTS WT JI 0.9566
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
07
Comparison and qualitative evidence
The optimized model is competitive while remaining lightweight.
Visual masks show strong overlap with ground truth; boundary errors are mostly localized around ambiguous tumor edges.
FBTS DSC improves over representative U-Net-based methods.
The method tunes the existing U-Net rather than adding a large architecture.
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
08
What the paper contributes
A compact optimization study, not only a final score.
1
Lightweight GWO tuning
small population and short evaluations
2
Parameterized U-Net
architecture + training variables
3
Behavior analysis
convergence, sensitivity, Top-K region
4
Generalization check
FBTS and BraTS 2021 evaluation
Key message
Metaheuristic tuning can improve segmentation quality while keeping the final U-Net compact and interpretable.
ICCS 2026 | DESY, Hamburg | 29 June - 1 July 2026
09
Take-home message
A lightweight GWO search can identify high-performing U-Net settings under limited computational budget.
Best FBTS score: DSC 0.9820.
Best BraTS 2021 whole-tumor score: DSC 0.9778.
The most informative parameter was the learning rate; moderate depth was most stable.
Acknowledgement
ACK Cyfronet AGH
Grant no. PLG/2025/018784
Polish Ministry of Science and Higher Education funds assigned to AGH University of Krakow
Thank you
Questions?