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Care of terminally ill / Palliative care

Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.

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Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning

While Vision-Language Models (VLMs) exhibit exceptional 2D visual understanding, their ability to co...

An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes f...

GSNR: Graph Smooth Null-Space Representation for Inverse Problems

Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the ...

Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation

Visual loco-manipulation of arbitrary objects in the wild with humanoid robots requires accurate end...

LEADER: Lightweight End-to-End Attention-Gated Dual Autoencoder for Robust Minutiae Extraction

Minutiae extraction, a fundamental stage in fingerprint recognition, is increasingly shifting toward...

MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification

Whole slide images (WSIs) are the gold standard for pathological diagnosis and sub-typing. Current m...

Ctrl&Shift: High-Quality Geometry-Aware Object Manipulation in Visual Generation

Object-level manipulation, relocating or reorienting objects in images or videos while preserving sc...

MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning

Metaphorical comprehension in images remains a critical challenge for Nowadays AI systems. While Mul...

Structural phenotypes of osteoarthritis are clinically and genetically distinct: findings from 59,539 UK Biobank participants

OBJECTIVES Osteoarthritis is a heterogeneous disease, with diverse structural patterns likely reflec...

AD$^2$: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems

End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robu...

Model-Driven Hybrid AI Framework for End-to-End Autonomous Decision-Making in Drug Development

Decision-making in drug development spans heterogeneous stages from molecular design to clinical opt...

Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases

Optical coherence tomography (OCT) has revolutionized retinal disease diagnosis with its high-resolu...

End-to-end reconstruction of OCT optical properties and speckle-reduced structural intensity via physics-based learning

Inverse scattering in optical coherence tomography (OCT) seeks to recover both structural images and...

Developing and externally validating machine learning models to forecast short-term risk of ventilator-associated pneumonia

Purpose: Ventilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired inf...

OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

The development of large vision language models drives the demand for managing, and applying massive...

ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing

Charts are a fundamental visualization format for structured data analysis. Enabling end-to-end char...

Li-ViP3D++: Query-Gated Deformable Camera-LiDAR Fusion for End-to-End Perception and Trajectory Prediction

End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities ...

NuiWorld: Exploring a Scalable Framework for End-to-End Controllable World Generation

World generation is a fundamental capability for applications like video games, simulation, and robo...

SqueakPose Studio: An end-to-end platform for pose estimation and real-time edge-AI deployment

Accurate pose estimation underpins quantitative analysis of behavior, yet many deep learning-based t...

End-to-end deep learning versus machine learning for biomarker discovery in cancer genomes

Background: Accurate determination of genomic biomarkers from tumor sequencing is fundamental to pre...

Learning temporal embeddings from electronic health records of chronic kidney disease patients

We investigate whether temporal embedding models trained on longitudinal electronic health records c...

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