AIMC Topic: Image Processing, Computer-Assisted

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Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Journal of X-ray science and technology
Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adve...

ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.

Tomography (Ann Arbor, Mich.)
BACKGROUND: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-...

Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.

Scientific reports
Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, l...

CycleH-CUT: an unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning.

Physics in medicine and biology
Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based methods have proven effective in unpaired medical image translation. However, these methods can produce ar...

Exploring the values underlying machine learning research in medical image analysis.

Medical image analysis
Machine learning has emerged as a crucial tool for medical image analysis, largely due to recent developments in deep artificial neural networks addressing numerous, diverse clinical problems. As with any conceptual tool, the effective use of machine...

AI in Breast Cancer Imaging: An Update and Future Trends.

Seminars in nuclear medicine
Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging by enhancing diagnostic capabilities across multiple imaging modalities including mammography, digit...

CephTransX: An attention enhanced feature fusion network leveraging neighborhood rough set approach for cephalometric landmark prediction.

Computers in biology and medicine
The convergence of medical imaging, computer vision, and orthodontics has made automatic cephalometric landmark detection a pivotal area of research. Accurate cephalometric analysis is crucial in orthodontics, orthognathic and maxillofacial surgery f...

Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening.

Computers in biology and medicine
BACKGROUND: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects ...

MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

Sensors (Basel, Switzerland)
The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple image processing techniques with radiologists' visual assessments of magnetic resonance imaging (MRI...

Deep learning-based Intraoperative MRI reconstruction.

European radiology experimental
BACKGROUND: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.