# Scalable Neural Architecture Search for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/1906.05956 · HTML: https://mlsanity.com/paper/1906.05956/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 21 of 21)

- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation](https://mlsanity.com/paper/2112.11065/) (2021)
- [PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution](https://mlsanity.com/paper/2204.11797/) (2022)
- [PanTS: The Pancreatic Tumor Segmentation Dataset](https://mlsanity.com/paper/2507.01291/) (2025)
- [HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet](https://mlsanity.com/paper/2112.10652/) (2021)
- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data](https://mlsanity.com/paper/2510.14831/) (2025)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification](https://mlsanity.com/paper/2101.07429/) (2021)
- [Graph-based Neural Architecture Search with Operation Embeddings](https://mlsanity.com/paper/2105.04885/) (2021)
- [MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures](https://mlsanity.com/paper/2102.13280/) (2021)
- [LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds](https://mlsanity.com/paper/2210.05018/) (2022)
- [HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2412.11458/) (2024)
- [Probeable DARTS with Application to Computational Pathology](https://mlsanity.com/paper/2108.06859/) (2021)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.06697/) (2022)
- [Active-DiNTS: Active Differentiable Network Topology Search](https://mlsanity.com/paper/2610.04787/) (2026)

## Abstract (arXiv)

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space. Our NAS framework searches the structure of each layer including neural connectivities and operation types in both of the encoder and decoder. Since optimizing over a large discrete architecture space is difficult due to high-resolution 3D medical images, a novel stochastic sampling algorithm based on a continuous relaxation is also proposed for scalable gradient based optimization. On the 3D medical image segmentation tasks with a benchmark dataset, an automatically designed architecture by the proposed NAS framework outperforms the human-designed 3D U-Net, and moreover this optimized architecture is well suited to be transferred for different tasks.