AIMC Topic: Algorithms

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A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Sparse arrays offer several advantages over other element reduction techniques for 3-D ultrasound imaging. However, the large interelement spacing in these arrays results in high sidelobe-related artifacts, which significantly degrade image quality a...

ChatIOS: Improving automatic 3-dimensional tooth segmentation via GPT-4V and multimodal pre-training.

Journal of dentistry
OBJECTIVES: This study aims to propose a framework that integrates GPT-4V, a recent advanced version of ChatGPT, and multimodal pre-training techniques to enhance deep learning algorithms for 3-dimensional (3D) tooth segmentation in scans produced by...

Global-Local Transformer Network for Automatic Retinal Pathological Fluid Segmentation in Optical Coherence Tomography Images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As a pivotal biomarker, the accurate segmentation of retinal pathological fluid such as intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), was a critical task for diagnosis and treatme...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

Computer methods and programs in biomedicine
OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

Predicting host-pathogen interactions with machine learning algorithms: A scoping review.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
BACKGROUND: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protei...

A hybrid PKI and spiking neural network approach for enhancing security and energy efficiency in IoMT-based healthcare 5.0.

SLAS technology
In the rapidly evolving field of healthcare 5.0, the Internet of Medical Things (IoMT) is expected to be an enabler that allows smart medical devices to collaborate and communicate with healthcare networks to speed up procedures, enhance care, and im...

Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning.

Computers in biology and medicine
Brain tumors have a great impact on patients' quality of life and accurate histopathological classification of brain tumors is crucial for patients' prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide i...

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification.

Journal of biomedical informatics
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simp...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

SLAS technology
The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Mode...

multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information.

Computers in biology and medicine
Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities...