BACKGROUND: Utilizing automated systems for diagnosing malignant skin lesions promises to improve the early detection of skin diseases and increase patients' survival rates. However, current classification methods primarily focus on global features, ...
In the Retinal Image Vessel (RIV) segmentation task, due to existing a large number of low-contrast capillaries in the image usually leads to the problem of poor segmentation accuracy. To address this issue, this study aims to fully model the global ...
The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires ...
BACKGROUND: Existing tools for reference retrieval using large language models (LLMs) frequently generate inaccurate, gray literature or fabricated citations, leading to poor accuracy. In this study, we aim to address this gap by developing a highly ...
The pharmaceutical industry faces persistent challenges in developing effective treatments for complex diseases, creating an urgent need for innovative approaches to accelerate drug discovery. A pivotal factor in this process is the accurate predicti...
This study presents a novel artificial intelligence approach for detecting wisdom teeth in panoramic radiographs using a multi-channel convolutional neural network (CNN). First, a curated dataset of annotated panoramic dental images was collected, wi...
Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised learning, which requires significant manual effort to generate ground truth labels. Recently, weakl...
Deep learning label-free cell imaging has become essential in modern medical applications, enabling precise cell analysis while preserving natural biological functions and structures by removing the need for potentially disruptive staining reagents. ...
Speech and voice have emerged as valuable non-invasive biomarkers for detecting and monitoring a range of medical conditions, from neurodegenerative and respiratory diseases to psychiatric and emotional disorders. Recent advancements in artificial in...
We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. AF, a major cardiac arrhythmia affecting millions globally, requires early detection for optimal treatment outcomes. Current rhyt...