Learning Robust Medical Image Segmentation with Inductive Bias
Abstract
Despite the success of transformer-based and convolutional neural networks in 3D medical image segmentation, current architectures exhibit limited generalisation on small datasets and under distribution shifts, especially when high-quality examples are scarce for specific structures. We introduce IB-nnU-Nets, a family of U-Net variants augmented with inductively biased filters inspired by vertebrate visual processing. Starting from a 3D U-Net backbone, we insert two 3D residual components into the second encoder block that implement on- and off-centre-surround convolutions with fixed, pre-computed weights and act as complementary edge detectors. Across multiple organ and tumour segmentation tasks, we show that equipping state-of-the-art 3D U-Nets with an IB block improves accuracy and robustness, with the strongest gains in small-data and out-of-distribution settings. The framework and trained IB-nnU-Net models are publicly available.
Cite
Text
Bhandary et al. "Learning Robust Medical Image Segmentation with Inductive Bias." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.Markdown
[Bhandary et al. "Learning Robust Medical Image Segmentation with Inductive Bias." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.](https://mlanthology.org/midl/2026/bhandary2026midl-learning/)BibTeX
@inproceedings{bhandary2026midl-learning,
title = {{Learning Robust Medical Image Segmentation with Inductive Bias}},
author = {Bhandary, Shrajan and Kuhn, Dejan and Babaiee, Zahra and Fechter, Tobias and Grosu, Anca-Ligia and Grosu, Radu},
booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning},
year = {2026},
pages = {3355-3373},
volume = {315},
url = {https://mlanthology.org/midl/2026/bhandary2026midl-learning/}
}