AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges.

Computers in biology and medicine
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, an...

Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection.

Computers in biology and medicine
The mortality risk associated with breast cancer is experiencing an exponential rise, underscoring the critical importance of early detection. It is the primary cause of mortality among women under 50 and ranks as the second deadliest disease globall...

Deep learning-based LDL-C level prediction and explainable AI interpretation.

Computers in biology and medicine
This study investigates the use of deep learning (DL) models to predict low-density lipoprotein cholesterol (LDL-C) levels. The dataset obtained from New York-Presbyterian Hospital/Weill Cornell Medical Center includes triglycerides (TG), total chole...

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 ...

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review.

Computers in biology and medicine
The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling ...

Improving explanations for medical X-ray diagnosis combining variational autoencoders and adversarial machine learning.

Computers in biology and medicine
Explainability in Medical Computer Vision is one of the most sensible implementations of Artificial Intelligence nowadays in healthcare. In this work, we propose a novel Deep Learning architecture for eXplainable Artificial Intelligence, specially de...

A novel coarsened graph learning method for scalable single-cell data analysis.

Computers in biology and medicine
The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectivene...

Prediction and detection of terminal diseases using Internet of Medical Things: A review.

Computers in biology and medicine
The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full po...

Explaining Human Activity Recognition with SHAP: Validating insights with perturbation and quantitative measures.

Computers in biology and medicine
In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP) to explain the decision-making process of Graph Convolution Networks (...