# BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2003.01581 · HTML: https://mlsanity.com/paper/2003.01581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Gen-Fab: A Variation-Aware Generative Model for Predicting Fabrication Variations in Nanophotonic Devices](https://mlsanity.com/paper/2603.11505/) (2026)
- [Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation](https://mlsanity.com/paper/2608.05815/) (2026)

## Abstract (arXiv)

In recent years, convolutional neural networks (CNNs) have revolutionized medical image analysis. One of the most well-known CNN architectures in semantic segmentation is the U-net, which has achieved much success in several medical image segmentation applications. Also more recently, with the rise of autoML ad advancements in neural architecture search (NAS), methods like NAS-Unet have been proposed for NAS in medical image segmentation. In this paper, with inspiration from LadderNet, U-Net, autoML and NAS, we propose an ensemble deep neural network with an underlying U-Net framework consisting of bi-directional convolutional LSTMs and dense connections, where the first (from left) U-Net-like network is deeper than the second (from left). We show that this ensemble network outperforms recent state-of-the-art networks in several evaluation metrics, and also evaluate a lightweight version of this ensemble network, which also outperforms recent state-of-the-art networks in some evaluation metrics.