KAN-AINet: Kolmogorov–Arnold Network with Adaptive Illumination Modulation for Generalizable Polyp Segmentation

Model Description

KAN-AINet is a polyp segmentation architecture that leverages Kolmogorov–Arnold Networks (KAN) for adaptive illumination modulation and boundary-aware attention (MICCAI 2026). Unlike standard neural networks that use fixed activation functions, KAN learns optimal per-task activation functions, enabling more expressive feature transformations for challenging colonoscopy images.

It introduces two KAN-based modules:

• KAN-IMM (Illumination Modulation Module): adaptive illumination modulation that improves robustness under dark, medium, and bright conditions (largest gain under extreme lighting, p = 0.037).
• KAN-BAM (Boundary Attention Module): multi-scale edge-aware attention (3×3, 5×5, 7×7 receptive fields) that differentiates true polyp boundaries from illumination artifacts.

KAN-based activation functions are directly visualizable, providing interpretability into how the network adapts its feature transformations for segmentation.

Training Details

• Architecture: KAN-AINet (KAN-IMM + KAN-BAM modules)
• Training dataset: Same as ESPNet, available from the ESPNet Polyp Segmentation repository
• Configuration: Default settings or modifiable hyperparameters in config.py; trained via train_threshold.py
• External benchmarks (unseen): Kvasir-Sessile, CVC-ColonDB, ETIS-LaribPolypDB, PolypGen-C6

Model Performance

Evaluated on unseen external validation datasets with segmentation-accuracy and boundary-based metrics (mDice, mIoU, Sα, Fβ^w, MAE, HD95, ASD, Precision, Recall, Specificity):

Metric	Result
mDice	+4.99% over prior SOTA
mIoU	+5.07% over prior SOTA
HD95 (KAN-BAM)	−33.7% vs. variant without KAN
ASD (KAN-BAM)	−42.95% vs. variant without KAN
Prediction variance (Brown–Forsythe)	ratio 0.68, p < 0.001

• Improves mDice by 4.99% and mIoU by 5.07% over prior SOTA on external benchmarks
• KAN-IMM yields the largest gains under extreme lighting (p = 0.037)
• KAN-BAM reduces HD95 and ASD by 33.7% and 42.95% over the no-KAN variant
• Brown–Forsythe testing confirms significantly lower prediction variance across all illumination conditions, demonstrating stable, trustworthy performance

Absolute per-dataset scores are reported in the comparison table in the source repository.

Download & Use

Download the checkpoint from the Hugging Face repo:

from huggingface_hub import hf_hub_download
from models.kan_acnet import KANACNet, visualize

model_path = hf_hub_download(repo_id="biodatlab/kan-ainet", filename="model.pth")

kan  = KANACNet(model_path)   # loads weights, eval mode, auto GPU/CPU
mask = kan("test.jpg")        # numpy uint8 array
visualize("test.jpg", mask)   # displays the result

KANACNet comes from the source repo — clone it and pip install -r requirements.txt first.

Intended Use

• Research on illumination-robust, generalizable polyp segmentation in colonoscopy images.
• Benchmarking against polyp segmentation baselines on external/unseen datasets.
• Support for boundary-accurate and interpretable segmentation in colonoscopy analysis pipelines.

Limitations

• Research model — not a medical device; not for clinical diagnosis, screening, or treatment decisions.
• Trained on the ESPNet polyp segmentation data; performance on imaging modalities, scopes, or populations outside the evaluated benchmarks is not characterized.
• Outputs require expert clinical review before any patient-facing use.
• As with any deep learning system, risks include errors and domain shifts under conditions unlike the training/evaluation data.

Acknowledgments

Developed by the Biomedical and Data Lab (biodatlab) with the collaboartion with Diagnostic Intelligence Group (DIG) at University of Alabama at Birmingham. We acknowledge the broader open-source community whose tools and prior work on KAN, polyp segmentation, and the ESPNet dataset made this project possible.

Code, training, and full results: https://github.com/biodatlab/kanainet