Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventional embodied frameworks rely on low-dimensional structured action vectors (e.g., joint angles and end-effector poses), which suffer from limited expressive capacity, poor generalization across diverse embodiments, and unnatural dynamic modeling for c...
Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture s...
Microscopy image analysis is central to modern biology, yet many available platforms remain inaccessible to non-specialist users because they require ...
Hyperspectral imaging provides rich spectral information for quantitative remote sensing, yet hyperspectral sensors remain costly and thus unavailable...
Cell-type extraction is an important task in biomedical text mining because biomedical literature contains evidence about cell types and cell-type-rel...
Protein structure prediction via AlphaFold2 has revolutionized drug discovery, yet its end-to-end execution remains computationally intensive. While G...
Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across...
The problem of object pose and shape estimation has seen key advancements lately. Encoder-decoder (e.g., SAM3D, LRM, CRISP) and diffusion-based models...
While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational met...
Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because ...
Real-world image restoration is challenging due to complex and interacting mixed degradations. Recent agent-based approaches address this problem by c...
While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reaso...
Membrane transport is a fundamental biological process with profound implications for pharmacology, biotechnology, and microbiology. While computation...
Translating transcriptomic data into therapeutic hypotheses remains fragmented and labor-intensive. Here we present ConvergeCELL, a platform combining...
Three-dimensional (3D) whole-organ imaging and analysis at cellular resolution (termed 3D histology) provide profound insights into the organization a...
RGB-to-hyperspectral image reconstruction is a highly ill-posed inverse problem, since multiple plausible spectral distributions may correspond to the...
Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative ...
Domain generalization requires identifying stable representations that support reliable classification across domains. Most existing methods seek such...
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations f...
End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decision...