# BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2106.14033 · HTML: https://mlsanity.com/paper/2106.14033/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [CARS: Continuous Evolution for Efficient Neural Architecture Search](https://mlsanity.com/paper/1909.04977/) (2019)
- [Recurrent U-Net for Resource-Constrained Segmentation](https://mlsanity.com/paper/1906.04913/) (2019)
- [BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture](https://mlsanity.com/paper/2007.00243/) (2020)
- [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2007.06151/) (2020)

## Built on by (most cited first, 13 of 13)

- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing](https://mlsanity.com/paper/2309.06047/) (2023)
- [Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond](https://mlsanity.com/paper/2203.05709/) (2022)
- [RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2206.04682/) (2022)
- [MRF-UNets: Searching UNet with Markov Random Fields](https://mlsanity.com/paper/2207.06168/) (2022)
- [Exploring Generalizable Distillation for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.12995/) (2022)
- [CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image](https://mlsanity.com/paper/2208.10701/) (2022)
- [Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data](https://mlsanity.com/paper/2210.04379/) (2022)
- [RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2506.02393/) (2025)
- [Meta-Learning of NAS for Few-shot Learning in Medical Image Applications](https://mlsanity.com/paper/2203.08951/) (2022)
- [LightSNN: Lightweight Architecture Search for Sparse and Accurate Spiking Neural Networks](https://mlsanity.com/paper/2503.21846/) (2025)
- [Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation](https://mlsanity.com/paper/2605.08238/) (2026)

## Abstract (arXiv)

The recurrent mechanism has recently been introduced into U-Net in various medical image segmentation tasks. Existing studies have focused on promoting network recursion via reusing building blocks. Although network parameters could be greatly saved, computational costs still increase inevitably in accordance with the pre-set iteration time. In this work, we study a multi-scale upgrade of a bi-directional skip connected network and then automatically discover an efficient architecture by a novel two-phase Neural Architecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method reduces the network computational cost by sifting out ineffective multi-scale features at different levels and iterations. We evaluate BiX-NAS on two segmentation tasks using three different medical image datasets, and the experimental results show that our BiX-NAS searched architecture achieves the state-of-the-art performance with significantly lower computational cost.