AIMC Topic: Deep Learning

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Deep learning for giant cell arteritis diagnosis on temporal artery biopsy.

Computers in biology and medicine
OBJECTIVES: Giant Cell Arteritis (GCA) is a vasculitis affecting large and medium-caliber arteries, requiring early and accurate diagnosis to prevent serious complications. Temporal artery biopsy (TAB) is the gold standard for histopathological diagn...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Computers in biology and medicine
Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users mu...

Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

Computers in biology and medicine
BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded image quality, compromising diagnostic reliability. Recent advances in deep learning have shown promis...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Radiology. Artificial intelligence
Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials a...

Efficient Ultrasound Breast Cancer Detection with DMFormer: A Dynamic Multiscale Fusion Transformer.

Ultrasound in medicine & biology
OBJECTIVE: To develop an advanced deep learning model for accurate differentiation between benign and malignant masses in ultrasound breast cancer screening, addressing the challenges of noise, blur, and complex tissue structures in ultrasound imagin...

Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning models have been widely applied to ECG classification tasks, their accuracy remains limited, especially i...

AMeta-FD: Adversarial Meta-learning for Few-shot retinal OCT image Despeckling.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Speckle noise in Optical coherence tomography (OCT) images compromises the performance of image analysis tasks such as retinal layer boundary detection. Deep learning algorithms have demonstrated the advantage of being more cost-effective and robust ...

Exploring advanced deep learning approaches in cardiac image analysis: A comprehensive review.

Computers in biology and medicine
BACKGROUND: Cardiac image analysis plays an important role in detecting and categorizing cardiovascular diseases (CVDs), such as coronary artery disease (CAD), heart failure, congenital heart defects, arrhythmias (irregular heartbeat), and valvular h...

A comprehensive targeted panel of 295 genes: Unveiling key disease initiating and transformative biomarkers in multiple myeloma.

Computers in biology and medicine
BACKGROUND: Multiple myeloma (MM) is a hematological malignancy that progresses from a benign precursor stage known as Monoclonal Gammopathy of Undetermined Significance (MGUS). Distinguishing MM from MGUS at the molecular level by identifying key bi...

CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...