AIMC Topic: Deep Learning

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Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.

European journal of radiology
OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.

New approaches to lesion assessment in multiple sclerosis.

Current opinion in neurology
PURPOSE OF REVIEW: To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their imp...

Automatic deep learning segmentation of mandibular periodontal bone topography on cone-beam computed tomography images.

Journal of dentistry
OBJECTIVES: This study evaluated the performance of a multi-stage Segmentation Residual Network (SegResNet)-based deep learning (DL) model for the automatic segmentation of cone-beam computed tomography (CBCT) images of patients with stage III and IV...

Automated Microbubble Discrimination in Ultrasound Localization Microscopy by Vision Transformer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound localization microscopy (ULM) has revolutionized microvascular imaging by breaking the acoustic diffraction limit. However, different ULM workflows depend heavily on distinct prior knowledge, such as the impulse response and empirical sele...

Learning-Based Classification of B- and T-Cell Lymphoma on Histopathological Images: A Multicenter Study.

European journal of haematology
Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limited biopsy specimens often restrict the use of multiple stains, complicating diagnostic workflows. Lym...

Development of a deep learning method for phase retrieval image enhancement in phase contrast microcomputed tomography.

Journal of microscopy
Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified...

AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Measuring the thickness of the glomerular basement membrane (GBM) and assessing the percentage of podocyte foot process effacement (%PFPE) are important for diagnosing non-neoplastic kidney diseases. However, when performed ...

A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.

Journal of neuroscience methods
BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the signific...

Machine learning-based approaches for distinguishing viral and bacterial pneumonia in paediatrics: A scoping review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding approp...

Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach.

Artificial intelligence in medicine
Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detection is crucial for timely intervention. However, it remains challenging due to its asymptomatic pro...