Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
Visual generative models are typically trained in two stages. A tokenizer is first trained for reconstruction and then frozen, after which a generator is trained on its discrete indices or continuous latents. This decoupling leaves the tokenizer unaware of what the generator finds easy to model. We present GEAR (Guided End-to-end AutoRegression), which trains a vector-quantized (VQ) tokenizer and ...
Accurate identification of resistor values from unconstrained images remains a challenging computer vision task due to variations in lighting, orientation, scale, and background complexity. This paper presents HiRes, a hierarchical cascaded pipeline for end-to-end resistor value identification directly from full-frame images. The approach combines object detection (YOLOv8n), semantic segmentation ...
Can the robot use a plate to cut a cake if no knife is available? Tool use greatly expands robot capabilities, but to use tools creatively beyond thei...
The accessible chemical space dwarfs any tractable screening budget, and most artificial intelligence drug discovery pipelines respond by docking and ...
Background: Metabolic syndrome (MetS) has been associated with cognitive decline. Considering its increasing prevalence worldwide, the goal of this st...
Manual reporting of 3D MRI studies is time-consuming, yet end-to-end structured report generation for 3D liver MRI remains underexplored due to volume...
Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often for...
Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated aut...
Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption: the scenes is entire ...
Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances in...
Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagn...
With the growing use of machine-learning-assisted pipelines for designing, characterizing, and optimizing biomolecules, the reliability of structure p...
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, howeve...
Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scen...
Existing vision encoders for robotics face a fundamental bottleneck: robotic datasets lack the scale necessary for large-scale pre-training. Prior wor...
We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generatio...
Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditionin...
This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structu...
Background and rationale: Knee osteoarthritis (KOA) is a leading cause of lower limb disability worldwide, characterized by functional limitations, st...
Spinal pathology is a leading cause of pain and disability worldwide. Spine magnetic resonance imaging (MRI) is central to clinical evaluation, yet it...