Latest AI and machine learning research in preventive care for healthcare professionals.
Accurate depth estimation enhances endoscopy navigation and diagnostics, but obtaining ground-truth depth in clinical settings is challenging. Synthetic datasets are often used for training, yet the domain gap limits generalization to real data. We propose a novel image-to-image translation framework that preserves structure while generating realistic textures from clinical data. Our key innovat...
Colonoscopy is vital in the early diagnosis of colorectal polyps. Regular screenings can effectively prevent benign polyps from progressing to CRC. While deep learning has made impressive strides in polyp segmentation, most existing models are trained on single-modality and single-center data, making them less effective in real-world clinical environments. To overcome these limitations, we propo...
In recent years, the rapid development of artificial intelligence (AI) has brought innovative opportunities to diabetes management, with significant a...
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen...
Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retina...
Group Ordered Weighted $L_{1}$-Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learnin...
Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, is a critical task with applications in pharmacovig...
Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications whi...
OBJECTIVE: Diabetic retinopathy (DR) screening rates are poor in remote Western Australia where communities rely on outdated primary care-based retina...
Background Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second bre...
The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associatio...
In colonoscopy, 80% of the missed polyps could be detected with the help of Deep Learning models. In the search for algorithms capable of addressing...
Early detection, accurate segmentation, classification and tracking of polyps during colonoscopy are critical for preventing colorectal cancer. Many...
Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural n...
As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies...
This study explores open questions in the application of machine learning for breast cancer detection in mammograms. Current approaches often employ...
Parkinson's disease (PD) is a prevalent neurodegenerative disorder globally. The eye's retina is an extension of the brain and has great potential i...
Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial fo...
Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hi...
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. Th...