Auto3DSeg for Brain Tumor Segmentation from 3D MRI in BraTS 2023 Challenge
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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.
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
6 of 9 references.
References · 6
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
- 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
- 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
- 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
- 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
- 2021DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentationcited by 48
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Citing papers per year: 2026: 2.
2 papers.