To perform accurate computer vision quality assessments of sperm used within reproductive medicine, a clear separation of each sperm component from the background is critical. This study systematically evaluates and compares the performance of Mask R...
Hae Kyung Im's research group focuses on quantitative computational and statistical methods to tackle genomic data analysis and provides methods to translate the vast amount of genomic data for health research. In collaboration with Mengjie Chen's gr...
Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here, we pr...
BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, partic...
BMC medical informatics and decision making
May 14, 2025
Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising death rate. Recently, Machine Learning (ML) and Deep Learning (DL) techniques have been utilized to detect and classify various types of cancer, inc...
Driver gaze estimation is important for various driver gaze applications such as building advanced driving assistance systems and understanding driver gaze behavior. Gaze estimation in terms of gaze zone classification requires large-scale labeled da...
Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The ...
Human activity recognition has complex applications because of its worldly use of acquisition devices, namely video cameras and smartphones, and its capability to take human activity data. Human activity recognition became a hot scientific subject in...
Deep learning, particularly convolutional neural networks (CNNs), has proven valuable for brain tumor classification, aiding diagnostic and therapeutic decisions in medical imaging. Despite their accuracy, these models are vulnerable to adversarial a...
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...
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