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Showing 9913-9933 of 14,672 articles

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data

Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) ...

Jul 2025 http://arxiv.org/abs/2507.06828v1

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation

PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morp...

Jul 2025 http://arxiv.org/abs/2507.07126v1

THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling

Wearable cameras are increasingly used as an observational and interventional tool for human behav...

Jul 2025 http://arxiv.org/abs/2507.06442v1

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography

Breast density assessment is a crucial component of mammographic interpretation, with high breast ...

Jul 2025 http://arxiv.org/abs/2507.06410v2

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machin...

Jul 2025 http://arxiv.org/abs/2507.06384v1

Miniaturized optically-generated Bessel beam ultrasound for volumetric transcranial brain stimulation

Non-invasive stimulation of small, variably shaped brain sub-regions is crucial for advancing our ...

Jul 2025 http://arxiv.org/abs/2507.06108v1

Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration

Cone-Beam Computed Tomography (CBCT) is widely used for intraoperative imaging due to its rapid ac...

Jul 2025 http://arxiv.org/abs/2507.06067v1

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization

Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access,...

Jul 2025 http://arxiv.org/abs/2507.05979v1

A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data

Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and c...

Jul 2025 http://arxiv.org/abs/2507.05883v1

Browse Categories

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation

THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

Miniaturized optically-generated Bessel beam ultrasound for volumetric transcranial brain stimulation

Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization

A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data

Modeling and Reversing Brain Lesions Using Diffusion Models

Learning Segmentation from Radiology Reports

Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift

Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods

Emerging Frameworks for Objective Task-based Evaluation of Quantitative Medical Imaging Methods

Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging.

A hybrid predictor-corrector network and spatiotemporal classifier method for noisy plant PET image classification.

Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS): A Review

Computed Tomography Visual Question Answering with Cross-modal Feature Graphing

Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need

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