Advanced imaging techniques, like magnetic resonance imaging (MRI), have revolutionised cardiovascular disease diagnosis and monitoring in humans and animal models. Real-time (RT) MRI, which can capture a single slice during each consecutive heartbea...
Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for un...
Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks has proven advantages over the traditional feature extraction techniques, it remains challenging due t...
The segmentation of liver margins in computed tomography (CT) images presents significant challenges due to the complex anatomical variability of the liver, with critical implications for medical diagnostics and treatment planning. In this study, we ...
Precise pest classification plays an essential role in smart agriculture. Crop yields are severely impacted by pest damage, which poses a critical challenge for agricultural production and the economy. Identifying pests is of utmost importance, but m...
Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning fra...
Facial Micro-Expression Recognition (FER) presents challenges due to individual variations in emotional intensity and the complexity of feature extraction. While apex frames offer valuable emotional information, their precise role in FER remains uncl...
With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate i...
Zebrafish are widely used in vertebrate studies, yet minimally invasive individual tracking and identification in the lab setting remain challenging due to complex and time-variable conditions. Advancements in machine learning, particularly neural ne...
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