# ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation

arXiv: https://arxiv.org/abs/2412.19713 · HTML: https://mlsanity.com/paper/2412.19713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [On-the-Fly Test-time Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2203.05574/) (2022)
- [Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images](https://mlsanity.com/paper/1911.00140/) (2019)
- [From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2310.20271/) (2023)
- [DLAU: A Scalable Deep Learning Accelerator Unit on FPGA](https://mlsanity.com/paper/1605.06894/) (2016)
- [Hybrid Cascaded Neural Network for Liver Lesion Segmentation](https://mlsanity.com/paper/1909.04797/) (2019)
- [CT Liver Segmentation via PVT-based Encoding and Refined Decoding](https://mlsanity.com/paper/2401.09630/) (2024)

## Built on by (most cited first, 1 of 1)

- [Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image Analysis](https://mlsanity.com/paper/2504.06811/) (2025)

## Abstract (arXiv)

The growing need for accurate and efficient 3D identification of tumors, particularly in liver segmentation, has spurred considerable research into deep learning models. While many existing architectures offer strong performance, they often face challenges such as overfitting and excessive computational costs. An adjustable and flexible architecture that strikes a balance between time efficiency and model complexity remains an unmet requirement. In this paper, we introduce proKAN, a progressive stacking methodology for Kolmogorov-Arnold Networks (KANs) designed to address these challenges. Unlike traditional architectures, proKAN dynamically adjusts its complexity by progressively adding KAN blocks during training, based on overfitting behavior. This approach allows the network to stop growing when overfitting is detected, preventing unnecessary computational overhead while maintaining high accuracy. Additionally, proKAN utilizes KAN's learnable activation functions modeled through B-splines, which provide enhanced flexibility in learning complex relationships in 3D medical data. Our proposed architecture achieves state-of-the-art performance in liver segmentation tasks, outperforming standard Multi-Layer Perceptrons (MLPs) and fixed KAN architectures. The dynamic nature of proKAN ensures efficient training times and high accuracy without the risk of overfitting. Furthermore, proKAN provides better interpretability by allowing insight into the decision-making process through its learnable coefficients. The experimental results demonstrate a significant improvement in accuracy, Dice score, and time efficiency, making proKAN a compelling solution for 3D medical image segmentation tasks.