HFEPX Hub
Updated from current HFEPX corpus (Feb 27, 2026). 13 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequently cited benchmark: Vbvr-Bench. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.
Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 13 papers for CS.CV + Medicine Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Vbvr-Bench and metric focus on accuracy, auroc. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.
15.4% of papers report explicit human-feedback signals, led by expert verification.
Evidence: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
automatic metrics appears in 92.3% of papers in this hub.
Evidence: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Vbvr-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.
Evidence: A Very Big Video Reasoning Suite , SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
Evidence: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Rater context is mostly domain experts, and annotation is commonly mixed annotation units; use this to scope replication staffing.
Evidence: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation , MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Track metric sensitivity by reporting both accuracy and auroc.
Evidence: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Papers with explicit human feedback
Coverage is a replication risk (15.4% vs 45% target).
Papers reporting quality controls
Coverage is a replication risk (0% vs 30% target).
Papers naming benchmarks/datasets
Coverage is a replication risk (7.7% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (61.5% vs 35% target).
Papers with known rater population
Coverage is strong (38.5% vs 35% target).
Papers with known annotation unit
Coverage is a replication risk (0% vs 35% target).
1. SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video
Start here for detailed protocol reporting, including rater and quality-control evidence.
Start here for detailed protocol reporting, including rater and quality-control evidence.
3. Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Start here for detailed protocol reporting, including rater and quality-control evidence.
4. Adversarial Robustness of Deep Learning-Based Thyroid Nodule Segmentation in Ultrasound
Adds automatic metrics for broader coverage within this hub.
5. FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
Adds automatic metrics for broader coverage within this hub.
6. XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
Adds automatic metrics for broader coverage within this hub.
7. MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Adds automatic metrics for broader coverage within this hub.
Adds automatic metrics for broader coverage within this hub.
automatic_metrics vs simulation_env
both=0, left_only=12, right_only=1
0 papers use both Automatic Metrics and Simulation Env.
Benchmark Brief
Vbvr-Bench
Coverage: 1 papers (7.7%)
1 papers (7.7%) mention Vbvr-Bench.
Examples: A Very Big Video Reasoning Suite
Metric Brief
accuracy
Coverage: 7 papers (53.8%)
7 papers (53.8%) mention accuracy.
Examples: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI , XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
Metric Brief
auroc
Coverage: 1 papers (7.7%)
1 papers (7.7%) mention auroc.
Examples: MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Metric Brief
coherence
Coverage: 1 papers (7.7%)
1 papers (7.7%) mention coherence.
Examples: KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Most Cited In This Hub
Fast path to methods with the strongest citation traction in this scope.
Papers: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Most Recent
Fast path to latest protocol changes and newly published evaluation setups.
Papers: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Best Protocol Detail
Papers with explicit rater/unit metadata and quality-control signals for reproducibility.
Papers: SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video , Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis , Virtual Biopsy for Intracranial Tumors Diagnosis on MRI
Guanyi Qin, Xiaozhen Wang, Zhu Zhuo, Chang Han Low, Yuancan Xiao · Feb 25, 2026 · Citations: 0
Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning.
Shaoxuan Wu, Jingkun Chen, Chong Ma, Cong Shen, Xiao Zhang · Feb 25, 2026 · Citations: 0
Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists.
Xinzhe Luo, Shuai Shao, Yan Wang, Jiangtao Wang, Yutong Bai · Feb 25, 2026 · Citations: 0
To address these challenges, we construct the ICT-MRI dataset - the first public biopsy-verified benchmark with 249 cases across four categories.
Nicholas Dietrich, David McShannon · Feb 25, 2026 · Citations: 0
Conclusion: Spatial-domain adversarial perturbations in ultrasound segmentation showed partial mitigation with input preprocessing, whereas frequency-domain perturbations were not mitigated by the defenses, highlighting modality-specific ch
Alina Devkota, Jacob Thrasher, Donald Adjeroh, Binod Bhattarai, Prashnna K. Gyawali · Feb 24, 2026 · Citations: 0
Extensive experiments on both natural and medical image benchmarking datasets, across diverse model architectures, demonstrate that FedVG consistently improves performance, particularly in highly heterogeneous settings.
Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, Mohammed Alser · Feb 24, 2026 · Citations: 0
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints.
Tianhao Fu, Yucheng Chen · Feb 24, 2026 · Citations: 0
MIPCandy provides a complete, modular pipeline spanning data loading, training, inference, and evaluation, allowing researchers to obtain a fully functional process workflow by implementing a single method, $\texttt{build_network}$, while r
Tian Lan, Lei Xu, Zimu Yuan, Shanggui Liu, Jiajun Liu · Feb 24, 2026 · Citations: 0
Our evaluation demonstrates that OrthoDiffusion achieves excellent performance in the segmentation of 11 knee structures and the detection of 8 knee abnormalities.
Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari, Berardino Barile, Yiming Xiao · Feb 23, 2026 · Citations: 0
Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts.
Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thaddäus Wiedemer · Feb 23, 2026 · Citations: 0
We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities.
Erfan Nourbakhsh, Nasrin Sanjari, Ali Nourbakhsh · Dec 9, 2025 · Citations: 0
Age-related macular degeneration (AMD) and choroidal neovascularization (CNV)-related conditions are leading causes of vision loss worldwide, with optical coherence tomography (OCT) serving as a cornerstone for early detection and managemen
Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn · Sep 9, 2025 · Citations: 0
Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance.
Zhanliang Wang, Da Wu, Quan Nguyen, Zhuoran Xu, Kai Wang · May 9, 2025 · Citations: 0
To address this challenge, we introduce MINT (Multimodal Integrated kNowledge Transfer), a framework that aligns unimodal large decoder models with domain-specific decision patterns from multimodal biomedical data through preference optimiz
