# Auto3DSeg for Brain Tumor Segmentation from 3D MRI in BraTS 2023 Challenge

arXiv: https://arxiv.org/abs/2510.25058 · HTML: https://mlsanity.com/paper/2510.25058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)

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

- [Fairboard: a quantitative framework for equity assessment of healthcare models](https://mlsanity.com/paper/2604.09656/) (2026)
- [Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI](https://mlsanity.com/paper/2606.14072/) (2026)

## Abstract (arXiv)

In this work, we describe our solution to the BraTS 2023 cluster of challenges using Auto3DSeg from MONAI. We participated in all 5 segmentation challenges, and achieved the 1st place results in three of them: Brain Metastasis, Brain Meningioma, BraTS-Africa challenges, and the 2nd place results in the remaining two: Adult and Pediatic Glioma challenges.