# Image Segmentation Using Hybrid Representations

arXiv: https://arxiv.org/abs/2004.07071 · HTML: https://mlsanity.com/paper/2004.07071/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

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

- [Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images](https://mlsanity.com/paper/2201.07935/) (2022)

## Abstract (arXiv)

This work explores a hybrid approach to segmentation as an alternative to a purely data-driven approach. We introduce an end-to-end U-Net based network called DU-Net, which uses additional frequency preserving features, namely the Scattering Coefficients (SC), for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts on four datasets and two segmentation tasks: Optic Disc and Optic Cup in color fundus images and fetal Head in ultrasound images. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods. The results indicate that it is possible to use a lighter network trained with fewer images (without any augmentation) to attain good segmentation results.