Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
Background: The FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance; however, its utility is constrained by data heterogeneity, pervasive reporting redundancies, and inconsistent medical terminology. These structural barriers impede reproducible, large-scale analyses and the implementation of precision drug safety surveillance. Methods: We developed ...
Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as recurring anatomical patterns or question-type regularities, rather than genuine diagnostic evidence. Existing causal approaches are typically implemented as static adjustments or post-hoc corrections. To address this issue, we propose a Learnable Causal ...
We present Feature-Align CNN (FA-CNN), a prototype CNN architecture with intrinsic class attribution through end-to-end feature alignment. Our intuiti...
Low-light image enhancement (LLIE) has traditionally been formulated as a deterministic mapping. However, this paradigm often struggles to account for...
Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditio...
End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world mult...
Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. Th...
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dyn...
Whole-aircraft diagnosis for general aviation faces threefold challenges: data uncertainty, task heterogeneity, and computational inefficiency. Existi...
The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires ...
Infrared and visible image fusion(IVIF) combines complementary modalities while preserving natural textures and salient thermal signatures. Existing s...
Diffusion models have achieved impressive performance on multi-focus image fusion (MFIF). However, a key challenge in applying diffusion models to the...
The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning ed...
Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailb...
We propose DepthTCM, a physics-aware end-to-end framework for depth map compression. In our framework of DepthTCM, the high-bit depth map is first con...
Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery,...
In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D...
Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains s...
Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing...
Accurate prediction of drug metabolites and enzyme selectivity is essential for rational drug design and safety assessment. However, existing computat...