AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Image Interpretation, Computer-Assisted

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A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Computers in biology and medicine
Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to ...

Navigating prevalence shifts in image analysis algorithm deployment.

Medical image analysis
Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current research emphasizes new training methods and network architectures, the specific impact of prevalence...

A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification.

Artificial intelligence in medicine
Skin cancer is frequently occurring and has become a major contributor to both cancer incidence and mortality. Accurate and timely diagnosis of skin cancer holds the potential to save lives. Deep learning-based methods have demonstrated significant a...

A novel generative model for brain tumor detection using magnetic resonance imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain tumors are a disease that kills thousands of people worldwide each year. Early identification through diagnosis is essential for monitoring and treating patients. The proposed study brings a new method through intelligent computational cells th...

Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification.

Computers in biology and medicine
BACKGROUND: Breast cancer is the most common cancer worldwide, and magnetic resonance imaging (MRI) constitutes a very sensitive technique for invasive cancer detection. When reviewing breast MRI examination, clinical radiologists rely on multimodal ...

Ensemble fuzzy deep learning for brain tumor detection.

Scientific reports
This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities. The method integrates multiple components, including dive...

Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.

Nuclear medicine communications
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

Journal of X-ray science and technology
OBJECTIVE: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses...

FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification.

Brain research
Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors....

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Scientific reports
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early d...