Artificial Intelligence Medical Compendium

Cone-beam computed tomography (CBCT) is a critical imaging modality in various medical fields, yet its repeated use poses radiation risks to patients. Low-dose CBCT image reconstruction aims to mitigate these risks while preserving image quality, whi... read more 

BACKGROUND: Accurate identification of benign and malignant bile duct dilatation (BDD) is needed to determine its management plan. Conventional imaging evaluation is subjective, whereas deep learning (DL) offers potential for automated objective asse... read more 

Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, variability in interpretation, and limited scalability. False negatives, false positives, and modera... read more 

The treatment of depression involves numerous barriers, both before and after individuals seek professional care. Online health communities (OHCs) have emerged as valuable platforms for people coping with depression. We collected 1,585,429 posts from... read more 

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Graph-based semi-supervised learning (GSSL) has received more attention in recent years. Many existing methods obtain a fixed similarity graph based on the original data. Affected by the redundant information and noise of the original data, the obtai... read more 

Foodborne pathogens represent a significant threat to public health. The development of rapid and sensitive detection methods is critical for the effective prevention and control of food safety issues. In recent years, pathogens detection methods bas... read more 

BACKGROUND: Lactylation is acknowledged as a regulator of numerous biological processes related to cancer. Research on the ability of lactylation-related genes (LacRGs) to predict prognosis and immunotherapeutic response in ovarian cancer (OC) patien... read more 

Recently, graph domain adaptation (GDA) has received significant attention. The goal is to transfer knowledge from a labelled source graph to an unlabelled target graph to alleviate the lack of high-quality labels in the target domain. However, curre... read more 

In the era of large language models (LLMs), low-rank adaptation (LoRA) has emerged as an essential technique for parameter-efficient fine-tuning, significantly reducing the computational and memory overhead during model adaptation. However, the poten... read more