AIMC Topic: Image Processing, Computer-Assisted

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AI-based open-source software for cephalometric analysis from limited FOV radiographs.

Journal of dentistry
BACKGROUND: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diag...

A systematic review of generalization research in medical image classification.

Computers in biology and medicine
Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and impleme...

Deep learning in disease vector image identification.

Pest management science
Vector-borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time-consuming and expert-dependent, hindering diseas...

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Neural networks : the official journal of the International Neural Network Society
Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oestrogen in menopausal women. Gradually, its symptoms worsen with age and prolonged menopausal status, which gravely impacts the quality of life as wel...

Optimized deep learning networks for accurate identification of cancer cells in bone marrow.

Neural networks : the official journal of the International Neural Network Society
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for disting...

Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy.

Magnetic resonance imaging
BACKGROUND: Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults. Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however...

Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.

Journal of neuro-oncology
INTRODUCTION: - Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (AI)-based models have improved the efficiency of these processes. This study evaluated an AI-based mo...

Multi-scale dual-channel feature embedding decoder for biomedical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical ima...

Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.

Medical & biological engineering & computing
The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditio...