Radiology

Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification.

OBJECTIVE: Automated, artificial intelligence (AI)-based, organ segmentation has the potential to streamline preclinical imaging workflows, but its suitability must be evaluated not only by geometric accuracy, but also by impact on downstream quantitative analyses. We validated a commercially available AI-based organ segmentation workflow for whole-body micro-computed tomography (micro-CT) data re...

Beyond Correlation: Causal Intervention for Multi-Label Medical Image Diagnosis.

This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, multiple conditions often co-occur, making multi-disease diagnosis more relevant than isolated single-disease cases. However, most deep learning methods focus on single-disease detection and fail to capture the complexity of diagnosing concurrent condit...

I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling.

Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit co...

Geometry aware neural radiance fields for freehand ultrasound reconstruction.

Reconstructing 3D volumes from 2D freehand ultrasound (US) is a challenging task. During reconstruction, the ensuing overlap between sweeps can cause ...

Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization.

Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic Reso...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible...

MSML-DenseXmer: harnessing vision transformers through integration with novel dense networks for medical image fusion.

The integration of multiple modalities in medical imaging allows a thorough representation of structural and functional details, resulting in improved...

Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach.

Injury detection and rehabilitation monitoring are critical components of sports medicine, particularly for gastrointestinal injuries that can impact ...

VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery.

Telesurgery integrates artificial intelligence (AI), robotic systems, sensing technologies, and wireless communication to enable remote and computer-a...

Autism spectrum disorder identification using machine learning models on MRI data.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an...

MSTM-Net: a two-stage prostate cancer segmentation network based on swin-transformer-mamba architecture.

BACKGROUND: Magnetic resonance imaging (MRI) has become a core imaging modality for prostate cancer screening and diagnosis. Accurate and automatic se...

A survey of deep learning techniques in detecting neurological disorders using MRI.

Magnetic resonance imaging (MRI) is widely regarded as the most reliable non-invasive imaging modality for detecting neurological disorders. However, ...

Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.

OBJECTIVES: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigat...

Intelligent documentation in medical education: can AI replace manual case logging?

OBJECTIVE: This study investigates the feasibility of using large language models (LLMs) to automate procedural case log documentation in radiology tr...

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis.

Recent advancements in artificial intelligence (AI) have significantly influenced the field of cardiovascular disease (CVD) analysis, particularly in ...

Deep Learning-Accelerated 3D FLAIR Enables Reliable MS Lesion Detection.

BACKGROUND AND PURPOSE: Deep learning-based reconstruction has the potential to shorten MRI acquisition while preserving diagnostic image quality, but...

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.

Making the decision between technically challenging partial nephrectomy (PN) and radical nephrectomy (RN) in patients with complex renal cell carcinom...

CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.

Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream model...

Uncertainty-Aware Super-Resolution for Mammography Phantoms using a Dropout-Enabled SwinIR.

This study was aimed at presenting a framework integrating uncertainty quantification into the SwinIR super-resolution model for mammography, addressi...

VMAM-NET: A Model Agnostic Meta-Learning Network for Rare De Novo Glioblastoma Diagnosis.

The diagnosis of grade IV brain tumors, such as de novo glioblastoma, has recently attracted a lot of scientific interest in neuroimaging and deep lea...