Neurosurgery

A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI

Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but have lagged behind on surgical image-analysis benchmarks. Since surgery requires integrating disparate tasks --- including multimodal data integration, human interaction, and physical effects --- generally-capable AI models could be particularly attractive as ...

Dissecting Model Failures in Abdominal Aortic Aneurysm Segmentation through Explainability-Driven Analysis

Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the model looks is the primary training signal, and thus we propose an Explainable AI (XAI) guided encoder shaping framework. Our method computes a dense, attribution-based encoder focus...

ARGENT: Adaptive Hierarchical Image-Text Representations

Large-scale Vision-Language Models (VLMs) such as CLIP learn powerful semantic representations but operate in Euclidean space, which fails to capture ...

GEAR: Geography-knowledge Enhanced Analog Recognition Framework in Extreme Environments

The Mariana Trench and the Qinghai-Tibet Plateau exhibit significant similarities in geological origins and microbial metabolic functions. Given that ...

Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features

We are concerned with the challenge of reliably classifying and assessing intracranial aneurysms using deep learning without compromising clinical tra...

Physics-Based Growth and Remodeling Modeling for Virtual Abdominal Aortic Aneurysm Evolution and Growth Prediction

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support ...

Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth

Multispectral demosaicing is crucial to reconstruct full-resolution spectral images from snapshot mosaiced measurements, enabling real-time imaging fr...

Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs

Reinforcement learning (RL) is widely used to improve large language models on reasoning tasks, and asynchronous RL training is attractive because it ...

Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization

Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institu...

Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift

Super-resolution is widely used in medical imaging to enhance low-quality data, reducing scan time and improving abnormality detection. Conventional s...

Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis

Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imag...

From Pre- to Intra-operative MRI: Predicting Brain Shift in Temporal Lobe Resection for Epilepsy Surgery

Introduction: In neurosurgery, image-guided Neurosurgery Systems (IGNS) highly rely on preoperative brain magnetic resonance images (MRI) to assist su...

Endoleak Prediction After EVAR: A Point Cloud Neural Network Framework Enhanced by Computational Fluid Dynamics and Multi-Features

Background: Endovascular aortic aneurysm repair (EVAR) is effective in preventing rupture of abdominal aortic aneurysm (AAA), but endoleak remains a s...

Deep Leakage with Generative Flow Matching Denoiser

Federated Learning (FL) has emerged as a powerful paradigm for decentralized model training, yet it remains vulnerable to deep leakage (DL) attacks th...

TrackletGPT: A Language-like GPT Framework for White Matter Tract Segmentation

White Matter Tract Segmentation is imperative for studying brain structural connectivity, neurological disorders and neurosurgery. This task remains c...

Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone

The way a person moves is a direct reflection of their neurological and musculoskeletal health, yet it remains one of the most underutilized vital s...

X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images

Integration of hyperspectral imaging into fluorescence-guided neurosurgery has the potential to improve surgical decision making by providing quanti...

ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data

The visualization of volumetric medical data is crucial for enhancing diagnostic accuracy and improving surgical planning and education. Cinematic r...

Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000

When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradient...

Intelligent Histology for Tumor Neurosurgery

The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standa...