RATIONALE AND OBJECTIVES: To evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI).
BMC medical informatics and decision making
Jun 4, 2025
BACKGROUND: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical setting...
OBJECTIVES: To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images.
AJNR. American journal of neuroradiology
Jun 3, 2025
BACKGROUND AND PURPOSE: The imaging of inflammatory myelopathies has advanced significantly across time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D do...
PURPOSE: To develop a self-supervised and memory-efficient deep learning image reconstruction method for 4D non-Cartesian MRI with high resolution and a large parametric dimension.
Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there i...
INTRODUCTION: The rise in cases of Gastric Cancer has increased in recent times and demands accurate and timely detection to improve patients' well-being. The traditional cancer detection techniques face issues of explainability and precision posing ...
To enhance polyp segmentation in colonoscopy images for early detection and diagnosis of colorectal cancer. The study proposed the Transformer-based cross feature multi-attention network (TCFMA-Net) for polyp segmentation by addressing challenges suc...
To verify the capability of the Segment Anything Model for medical images in 3D (SAM-Med3D), tailored with low-rank adaptation (LoRA) strategies, in segmenting breast tumors in Automated Breast Ultrasound (ABUS) images. This retrospective study colle...
SIGNIFICANCE: Diffuse correlation tomography (DCT) is an emerging technique for the noninvasive measurement of breast microvascular blood flow, whereas its capability to categorize benign and malignant breast lesions has not been extensively validate...
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