Latest AI and machine learning research in geriatrics for healthcare professionals.
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically ...
Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this study, we proposed two low-parameter Convolutional Neural Networks (CNNs), IR-BRAINNET and Modified-DEMNET, designed to detect the early stages of AD accurately....
Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic reson...
Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anom...
Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral d...
Surgical interventions, particularly in neurology, represent complex and high-stakes scenarios that impose substantial cognitive burdens on surgical...
The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This st...
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifyi...
We introduce a novel method for human shape and pose recovery that can fully leverage multiple static views. We target fixed-multiview people monito...
This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. I...
We present the first loss agent, dubbed LossAgent, for low-level image processing tasks, e.g., image super-resolution and restoration, intending to ...
The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enab...
As a sensitive functional imaging technique, positron emission tomography (PET) plays a critical role in early disease diagnosis. However, obtaining...
In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often ...
Understanding video content is pivotal for advancing real-world applications like activity recognition, autonomous systems, and human-computer inter...
The increasing global aging population necessitates improved methods to assess brain aging and its related neurodegenerative changes. Brain Age Gap ...
The adoption of Vision Transformers (ViTs) in resource-constrained applications necessitates improvements in inference throughput. To this end sever...
Early diagnosis and discovery of therapeutic drug targets are crucial objectives for the effective management of Alzheimer's Disease (AD). Current a...
Adversarial input image perturbation attacks have emerged as a significant threat to machine learning algorithms, particularly in image classificati...
Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust genera...