DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
22 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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 Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000 collectively.
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2026 22representative citing papers
SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
AnomalyClaw turns single-step VLM anomaly judgments into a multi-round tool-grounded refutation process, delivering consistent macro-AUROC gains of 3.5-7.9 percentage points over direct inference across 12 cross-domain datasets.
Introduces VietPET-RoI dataset with fine-grained RoI annotations for Vietnamese 3D PET/CT and HiRRA graph framework that improves report generation by modeling region dependencies, claiming large gains over prior models.
Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
A uniform benchmark across 77 experiments finds SRGAN superior to latent diffusion models for 3D medical image translation, with synthetic volumes indistinguishable from real ones in a 17-physician Turing test.
LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
BrainDINO delivers a single self-supervised brain MRI representation that generalizes to tumor segmentation, disease classification, brain age estimation, and other tasks without volumetric pretraining or full fine-tuning.
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
VS-DDPM accelerates 3D diffusion models for medical modality translation, reaching SOTA Dice scores of 0.80-0.88 and SSIM 0.95 on missing MRI synthesis in BraTS2025 while remaining competitive on tumor removal and sCT tasks.
WFM achieves near-diffusion quality for all four BraTS MRI modalities with one 82M model in 1-2 steps by flowing from the mean of conditioning modalities in wavelet space, running 250-1000x faster.
MedFlowSeg is a conditional flow matching model for medical image segmentation that adds dual-branch spatial attention and frequency-aware attention to achieve more efficient inference than diffusion models while improving structural consistency.
Replacing the generic Stable Diffusion VAE with domain-specific MedVAE pretrained on 1.6M medical images improves diffusion-based SR PSNR by 2.91-3.29 dB on knee/brain MRI and chest X-ray, with gains in fine details and VAE quality predicting SR performance (R²=0.67).
LAKE identifies sparse anomaly-sensitive neurons in pre-trained VLMs using minimal normal samples to build compact normality representations and achieve SOTA anomaly detection with neuron-level interpretability.
GCNV-Net achieves state-of-the-art accuracy on multiple 3D medical segmentation benchmarks while cutting FLOPs by 56% and inference latency by 68% through dynamic nonvoid voxelization and geometric attention.
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
A single model trained on one normal sample per dataset from nine heterogeneous medical sources achieves state-of-the-art anomaly detection in one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized settings.
SynerMedGen introduces generation-aligned understanding tasks and a two-stage training strategy that enables strong zero-shot medical image synthesis performance and outperforms specialized models when generation training is added.
RF-HiT uses rectified flow and a multi-scale hierarchical transformer to reach 91.27% Dice on ACDC and 87.40% on BraTS 2021 with only 10.14 GFLOPs, 13.6M parameters, and three inference steps.
A new Mean Shift Density Enhancement procedure applied to self-supervised embeddings yields state-of-the-art anomaly detection AUC and average precision on seven medical imaging datasets.
CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.
citing papers explorer
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DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
-
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
AnomalyClaw turns single-step VLM anomaly judgments into a multi-round tool-grounded refutation process, delivering consistent macro-AUROC gains of 3.5-7.9 percentage points over direct inference across 12 cross-domain datasets.
-
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Introduces VietPET-RoI dataset with fine-grained RoI annotations for Vietnamese 3D PET/CT and HiRRA graph framework that improves report generation by modeling region dependencies, claiming large gains over prior models.
-
Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Agentic LLMs autonomously execute complex neuro-radiological workflows like glioma segmentation and multi-timepoint response assessment by directing off-the-shelf tools, without any model training.
-
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
A uniform benchmark across 77 experiments finds SRGAN superior to latent diffusion models for 3D medical image translation, with synthetic volumes indistinguishable from real ones in a 17-physician Turing test.
-
LARGO: Low-Rank Hypernetwork for Handling Missing Modalities
LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
-
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
BrainDINO delivers a single self-supervised brain MRI representation that generalizes to tumor segmentation, disease classification, brain age estimation, and other tasks without volumetric pretraining or full fine-tuning.
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Diffusion Model as a Generalist Segmentation Learner
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
-
VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
VS-DDPM accelerates 3D diffusion models for medical modality translation, reaching SOTA Dice scores of 0.80-0.88 and SSIM 0.95 on missing MRI synthesis in BraTS2025 while remaining competitive on tumor removal and sCT tasks.
-
WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis
WFM achieves near-diffusion quality for all four BraTS MRI modalities with one 82M model in 1-2 steps by flowing from the mean of conditioning modalities in wavelet space, running 250-1000x faster.
-
MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention
MedFlowSeg is a conditional flow matching model for medical image segmentation that adds dual-branch spatial attention and frequency-aware attention to achieve more efficient inference than diffusion models while improving structural consistency.
-
Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution
Replacing the generic Stable Diffusion VAE with domain-specific MedVAE pretrained on 1.6M medical images improves diffusion-based SR PSNR by 2.91-3.29 dB on knee/brain MRI and chest X-ray, with gains in fine details and VAE quality predicting SR performance (R²=0.67).
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Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models
LAKE identifies sparse anomaly-sensitive neurons in pre-trained VLMs using minimal normal samples to build compact normality representations and achieve SOTA anomaly detection with neuron-level interpretability.
-
Geometrical Cross-Attention and Nonvoid Voxelization for Efficient 3D Medical Image Segmentation
GCNV-Net achieves state-of-the-art accuracy on multiple 3D medical segmentation benchmarks while cutting FLOPs by 56% and inference latency by 68% through dynamic nonvoid voxelization and geometric attention.
-
Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
-
Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection
A single model trained on one normal sample per dataset from nine heterogeneous medical sources achieves state-of-the-art anomaly detection in one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized settings.
-
SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment
SynerMedGen introduces generation-aligned understanding tasks and a two-stage training strategy that enables strong zero-shot medical image synthesis performance and outperforms specialized models when generation training is added.
-
RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
RF-HiT uses rectified flow and a multi-scale hierarchical transformer to reach 91.27% Dice on ACDC and 87.40% on BraTS 2021 with only 10.14 GFLOPs, 13.6M parameters, and three inference steps.
-
Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement
A new Mean Shift Density Enhancement procedure applied to self-supervised embeddings yields state-of-the-art anomaly detection AUC and average precision on seven medical imaging datasets.
-
CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.