IEEE journal of biomedical and health informatics
Jan 7, 2025
Molecular representation learning is of great importance for drug molecular analysis. The development in molecular representation learning has demonstrated great promise through self-supervised pre-training strategy to overcome the scarcity of labele...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. These movements can be identified with the General Movement Assessment, but their identification requires trained professionals to conduct the assessmen...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, ...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Melanoma is considered a global public health challenge and is responsible for more than 90% deaths related to skin cancer. Although the diagnosis of early melanoma is the main goal of dermoscopy, the discrimination between dermoscopic images of in s...
IEEE transactions on neural networks and learning systems
Jan 7, 2025
Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may ex...
IEEE transactions on neural networks and learning systems
Jan 7, 2025
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of ...
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has ...
Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within image...
Accurate semantic segmentation of histopathological images plays a crucial role in accurate cancer diagnosis. While fully supervised learning models have shown outstanding performance in this field, the annotation cost is extremely high. Weakly Super...
Journal of chemical information and modeling
Jan 6, 2025
Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity predict...