AIMC Topic: Image Interpretation, Computer-Assisted

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A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath.

American journal of physiology. Renal physiology
The presence of tubular casts within the kidney serves as an important feature when assessing the degree of renal injury. Quantification of renal tubular casts has been historically difficult due to varying cast morphologies, protein composition, and...

Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Magnetic resonance imaging
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption...

Interactively Fusing Global and Local Features for Benign and Malignant Classification of Breast Ultrasound Images.

Ultrasound in medicine & biology
OBJECTIVE: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. However, current convolutional neural networks (CNNs) face challenges in capturing global features, w...

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.

BMC medical imaging
INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-a...

Quantifying interpretation reproducibility in Vision Transformer models with TAVAC.

Science advances
Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction pow...

Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.

European radiology
OBJECTIVES: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological read...

Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

Magnetic resonance imaging
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...