Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge | Paper Citation Record

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Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks cites this paper.

Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 8

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Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI cites this paper.

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 16

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Multi-step Cascaded Networks for Brain Tumor Segmentation cites this paper.

Multi-step Cascaded Networks for Brain Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 11

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Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation cites this paper.

Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 4

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Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis cites this paper.

Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation cites this paper.

Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 1

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The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification cites this paper.

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 13

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Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion cites this paper.

Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2017

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Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline cites this paper.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 46

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cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis cites this paper.

cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation cites this paper.

MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 30

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HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation cites this paper.

HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 203

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop cites this paper.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 36

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Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room cites this paper.

Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 20

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Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation cites this paper.

Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2024

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge cites this paper.

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 1

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Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows cites this paper.

Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 30

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SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training cites this paper.

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 29

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FLIM-based Salient Object Detection Networks with Adaptive Decoders cites this paper.

FLIM-based Salient Object Detection Networks with Adaptive Decoders Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

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TumorTwin: A python framework for patient-specific digital twins in oncology cites this paper.

TumorTwin: A python framework for patient-specific digital twins in oncology Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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MedSG-Bench: A Benchmark for Medical Image Sequences Grounding cites this paper.

MedSG-Bench: A Benchmark for Medical Image Sequences Grounding Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 10

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Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities cites this paper.

Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 1

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PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings cites this paper.

PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

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DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis? cites this paper.

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis? Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 8

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Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding cites this paper.

Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective cites this paper.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 92

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crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023 cites this paper.

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023 Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2018

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BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis cites this paper.

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 8

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GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models cites this paper.

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 21

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Is Visual in-Context Learning for Compositional Medical Tasks within Reach? cites this paper.

Is Visual in-Context Learning for Compositional Medical Tasks within Reach? Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 12

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BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis cites this paper.

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 9

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Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentation cites this paper.

Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 16

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RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation cites this paper.

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 14

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Seeing It Before It Happens: In-Generation NSFW Detection for Diffusion-Based Text-to-Image Models cites this paper.

Seeing It Before It Happens: In-Generation NSFW Detection for Diffusion-Based Text-to-Image Models Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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Nexus-INR: Diverse Knowledge-guided Arbitrary-Scale Multimodal Medical Image Super-Resolution cites this paper.

Nexus-INR: Diverse Knowledge-guided Arbitrary-Scale Multimodal Medical Image Super-Resolution Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 53

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SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection cites this paper.

SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

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Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 80

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A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI cites this paper.

A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

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No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation cites this paper.

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 14

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RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans cites this paper.

RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 5

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Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review cites this paper.

Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 188

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SAMRI: Segment Any MRI cites this paper.

SAMRI: Segment Any MRI Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 56

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Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation cites this paper.

Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 25

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The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction cites this paper.

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 25

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Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space cites this paper.

Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2017

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Causal Transfer in Medical Image Analysis cites this paper.

Causal Transfer in Medical Image Analysis Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 179

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Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration cites this paper.

Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

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Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective cites this paper.

Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 11

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Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS) cites this paper.

Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS) Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c929fd93-ce1e-4f14-b3f2-a6d7cdbfce13 · inbound

Fairboard: a quantitative framework for equity assessment of healthcare models cites this paper.

Fairboard: a quantitative framework for equity assessment of healthcare models Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T21:07:57.988396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18766bc4-12c8-4dda-b831-05918415af0f · inbound

MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging cites this paper.

MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.267776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 012158da-69b9-45bf-9f60-ecfdcb7c00f1 · inbound

Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities cites this paper.

Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:17.655968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f70e5827-78e1-451d-9376-4fcd2ab95a0e · inbound

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection cites this paper.

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:01:09.322205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f3de38a-4aaa-4ff1-a990-0724ab13631f · inbound

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction cites this paper.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:50:26.777891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fab2fd22-e1ae-4c57-846d-fce24d6dd7dc · inbound

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction cites this paper.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-01T13:15:45.467379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5d7761a-fce0-4d0a-8c1b-8ebb760b68a3 · inbound

MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices cites this paper.

MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T11:01:29.784341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 060c9b9b-1b81-4444-a7b1-ed57c2aaeda1 · inbound

Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping cites this paper.

Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:25:52.851624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bfbb4762-ba99-41ba-9704-d78b0f3758f9 · inbound

Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping cites this paper.

Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T23:35:07.096116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4a964252-ba35-40ca-8e2a-79223290ae0e · inbound

MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift cites this paper.

MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:26:19.871150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 62419cac-44bc-40ac-bdee-b50a70450fed · inbound

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging cites this paper.

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T05:29:47.270420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2a6fa1a-687d-49c1-9e15-b2c816f63a41 · inbound

A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature cites this paper.

A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-19T18:37:42.864110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 66de0878-79ef-4ecf-9029-77ee6493bc3c · inbound

An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation cites this paper.

An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:21:12.696979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7db3fead-10c2-470e-a84a-018a5c5c8c00 · inbound

SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation cites this paper.

SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T07:11:12.838070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a09da0d6-2e49-4d6c-8246-69f470eb7097 · inbound

BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery cites this paper.

BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T09:13:16.101612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d791bd9-e538-44f4-9e60-5b9a19cc520c · inbound

A Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images cites this paper.

A Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:03:14.176545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d9bb627-96e2-4025-a60f-6a57a541c96c · inbound

Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning cites this paper.

Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 42

Resolution: metadata mismatch; local_arxiv, observed 2026-06-28T20:32:37.464733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fffc7ed5-310c-4312-84aa-b9dc3a780dd6 · inbound

DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation cites this paper.

DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T00:19:13.025305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e7813e47-81e8-4541-b416-2ce1d80241a5 · inbound

Towards Voxel Spacing Consistency for Medical Image Segmentation cites this paper.

Towards Voxel Spacing Consistency for Medical Image Segmentation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-07-01T10:05:41.103518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b5cc0d4-c532-400f-8674-d92f2b8939a4 · inbound

Mutually Exclusive Multiclass Lesion Segmentation in Neuroimaging: Binary-Guided Weak Supervision with Inter-Class Orthogonality cites this paper.

Mutually Exclusive Multiclass Lesion Segmentation in Neuroimaging: Binary-Guided Weak Supervision with Inter-Class Orthogonality Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-12T04:10:06.906293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25c2d19d-35f0-4e5f-8cac-05bae70d2195 · inbound

Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling cites this paper.

Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T14:48:46.294893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3bae7da-bce6-40ae-abbb-4659ce06d69f · inbound

Improving Medical Image Generative Models with Fr\'echet Distance Loss cites this paper.

Improving Medical Image Generative Models with Fr\'echet Distance Loss Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T05:39:36.813780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97877352-bd13-4b5e-a5c3-58412bd99987 · inbound

TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model cites this paper.

TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T03:42:57.902627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73e7c2fb-bfd5-4a5e-9e40-1db0c14d9957 · inbound

SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens cites this paper.

SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T16:27:28.008376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e710df8-b596-4968-adc7-fa492edbc1b9 · inbound

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation cites this paper.

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T11:42:25.810209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62c9f188-1742-42bd-b8e8-80b8b637e11b · inbound

A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor cites this paper.

A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-30T19:43:21.602531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e5e4072-487d-4a90-bf6e-7149f90cc738 · inbound

Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model cites this paper.

Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T14:08:13.372103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers