Brain Tumor Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

12 leaderboard tables shown for this task, 12 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 12 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

8 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 178 papers with code (436 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Attention U-Net: Learning Where to Look for the Pancreas
  11 Apr 2018 37 repositories listed Syntology ran 8 of 28 samples · 20 unverified · 7 pointer-only (licence)
  We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
• Brain Tumor Segmentation with Deep Neural Networks
  13 May 2015 14 repositories listed
  Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
• 3D MRI brain tumor segmentation using autoencoder regularization
  27 Oct 2018 7 repositories listed Syntology ran 0 of 18 samples · 18 unverified
  Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
• Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
  28 Feb 2018 7 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  Quantitative analysis of brain tumors is critical for clinical decision making.
• Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
  1 Sep 2017 7 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing…
• TBraTS: Trusted Brain Tumor Segmentation
  19 Jun 2022 4 repositories listed
  In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the…
• A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
  4 Nov 2020 4 repositories listed
  Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning.
• nnU-Net for Brain Tumor Segmentation
  2 Nov 2020 4 repositories listed
  We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
  9 Jun 2019 4 repositories listed
  Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
• SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
  6 Apr 2022 3 repositories listed
  Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
  4 Jan 2022 3 repositories listed
  Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression…
• Diffusion Models for Implicit Image Segmentation Ensembles
  6 Dec 2021 3 repositories listed Syntology ran 13 of 19 samples · 6 unverified
  By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.
• TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
  7 Mar 2021 3 repositories listed
  To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
• Autofocus Layer for Semantic Segmentation
  22 May 2018 3 repositories listed
  We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
• Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
  5 Dec 2024 2 repositories listed
  Additionally, an open-source web-application is accessible at https://segmenter.
• Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
  24 Nov 2024 2 repositories listed
  Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients.
• BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation
  14 Jun 2024 2 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  This study introduces a novel approach towards the creation of medical foundation models by integrating a large-scale multi-modal magnetic resonance imaging (MRI) dataset derived from 41, 400 participants in its own.
• SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
  15 Apr 2024 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence.
• MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
  1 Nov 2022 2 repositories listed
  Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
• Translation Consistent Semi-supervised Segmentation for 3D Medical Images
  28 Mar 2022 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data.
• Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
  24 Feb 2022 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
• TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images
  30 Jan 2022 2 repositories listed
  Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric…
• Optimized U-Net for Brain Tumor Segmentation
  7 Oct 2021 2 repositories listed
  We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge.
• The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
  5 Jul 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and…
• ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
  28 Jun 2021 2 repositories listed
  Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more…
• HMM Model for Brain Tumor Detection and Classification
  22 Jun 2021 2 repositories listed
  Brain tumor is one of the significant problems that has taken the life of a lot of people in recent times.
• Medical Image Segmentation Using Squeeze-and-Expansion Transformers
  20 May 2021 2 repositories listed Syntology ran 13 of 20 samples · 7 unverified · 20 pointer-only (licence)
  Medical image segmentation is important for computer-aided diagnosis.
• The Federated Tumor Segmentation (FeTS) Challenge
  12 May 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge…
• What is the best data augmentation for 3D brain tumor segmentation?
  26 Oct 2020 2 repositories listed
  Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
• Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
  14 Jul 2020 2 repositories listed
  To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a…

Syntology lines on 12 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.