Neurology

Head Trauma

Latest AI and machine learning research in head trauma for healthcare professionals.

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How (and How Not) to Use Data Augmentation in VLA Post-Training

Vision-language-action (VLA) models currently demonstrate strong performance in a wide range of real...

Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards

Recent text-to-image generation models have achieved remarkable visual quality, but improving them t...

VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models

Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and in...

Adversarial Training for Pixel Diffusion

Pixel diffusion models generate RGB images directly, avoiding the bottleneck of an autoencoder, yet ...

Optimizing and Securing the Modern Watermarking Channel for Images

To comply with recent regulations requiring traceable generated content, modern watermarking has ado...

One Sensor, Whole Body - 3D Body Pose from a Single Consumer Earbud IMU

Consumer earbuds already stream inertial motion data from the head, one of the most widely worn sens...

CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation

Accurate segmentation of post-treatment brain metastases is essential for treatment planning, longit...

A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards

When post-training large language models on tasks with semi-verifiable rewards, there are many facto...

ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation

Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing ...

PoEM: Predicting RL Outcomes from Existing Policies

Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, su...

Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images

We developed an anatomy-aware deep learning framework to synthesize post-contrast breast MRI from pr...

Visual Jev: Accurate and Efficient Decisions from Shared Visual Context

Many vision applications ask several independent, forced-choice questions about the same image. Visu...

Mammo-LIFE: Longitudinal Mammographic Imaging and Clinical Feature Enrichment for Post-Radiotherapy Outcome Prediction

Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have ...

Deep Learning Reconstruction versus Hybrid Iterative Reconstruction for Spectral CT: A Multi-Institutional Image Quality Assessment

Purpose: To compare image quality between deep learning reconstruction and hybrid iterative reconstr...

Automated Perceptually-Motivated Assessment of Photographic Consistency in Paired Clinical Photographs: Pipeline Development and Internal Evaluation

Purpose: Paired pre- and post-operative photographs are the standard unit of evidence for plastic su...

XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction

Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions ...

Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent-Aware Brain Parcellation

Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, s...

Prevalence calibration as shortcut mitigation

Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlati...

Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing

Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible hea...

Text Capability Loss in Vision-Language Adaptation: An Attention-Sink Diagnosis

Fine-tuning a pretrained LLM into a vision-language model (VLM) can erode the backbone's text capabi...

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