Long-context Multimodal Large Language Models (MLLMs) demand substantial
computational resources for inference as the growth of their multimodal Key-Value (KV) …

In medical Vision-Language Pre-training (VLP), significant work focuses on extracting text
and image features from clinical reports and medical images. Yet, existing methods may …

The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as
cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential …

Efficient inference in Large Language Models (LLMs) is impeded by the growing memory
demands of key-value (KV) caching, especially for longer sequences. Traditional KV cache …

The complexity and heterogeneity of data in many real-world applications pose significant
challenges for traditional machine learning and signal processing techniques. For instance …

Accurate sleep staging is crucial for wearable sensor-based sleep monitoring and health
interventions. Polysomnography (PSG) signals, rich in information from multiple …

Automatic radiology report generation can significantly benefit the labor-intensive process of
report writing by radiologists, especially for 3D radiographs like CT scans, which are crucial …

Machine learning algorithms have achieved remarkable success across various disciplines,
use cases and applications, under the prevailing assumption that training and test samples …

Electrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other
heart diseases, but deep learning methods based on ECG often face limitations due to the …

The advancement of Large Vision-Language Models (LVLMs) has propelled their
application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality …