Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
Accurate estimation of food nutrition plays a vital role in promoting healthy dietary habits and personalized diet management. Most existing food datasets primarily focus on Western cuisines and lack sufficient coverage of Chinese dishes, which restricts accurate nutritional estimation for Chinese meals. Moreover, many state-of-the-art nutrition prediction methods rely on depth sensors, restrictin...
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with recon...
Target trial emulation (TTE) enables causal inference from observational data but remains bottlenecked by manual, expert-dependent protocol operationa...
Traditional Active Noise Control (ANC) systems are mostly based on FxLMS algorithms, but such algorithms rely on linear assumptions and are often limi...
Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quanti...
Referring multi-object tracking (RMOT) is a task of associating all the objects in a video that semantically match with given textual queries or refer...
Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language ...
Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-to-...
Developing optical systems for free-space applications requires simulation tools that accurately capture turbulence-induced wavefront distortions and ...
Decreasing sequence length is a common way to accelerate transformers, but prior token reduction work often targets classification and reports proxy m...
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the...
Deep learning models utilizing longitudinal healthcare data have significantly advanced epidemiological research. However, contemporary transformer-ba...
End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies thr...
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale ...
This paper proposes an end-to-end shared attention estimation method via group detection. Most previous methods estimate shared attention (SA) without...
Protein language models (PLMs) are increasingly central to protein engineering and drug discovery. Many high-performing systems, however, rely on larg...
Optical character recognition remains critical infrastructure for document digitization, yet state-of-the-art performance is often restricted to well-...
With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and com...
Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for s...
Automatically extracting chemical structures from documents is essential for the large-scale analysis of the literature in chemistry. Automatic pipeli...