Latest AI and machine learning research in medicare for healthcare professionals.
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference without a separate check that geometric rarity makes it safe to trust. We probe this coupling with sparse training contamination. Under fixed representa- tions and memory budgets, we compare random, medoid, local, and global ...
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference without a separate check that geometric rarity makes it safe to trust. We probe this coupling with sparse training contamination. Under fixed representa- tions and memory budgets, we compare random, medoid, local, and global ...
Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, fol...
Extracting clinical information from Dutch free-text medical notes requires language-specific annotation resources, yet Dutch primary care lacks a reu...
Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, s...
We present a vision-only state estimation system for X-configuration quadcopters equipped with a canonical stereo camera pair and no inertial sensors....
Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These...
Vision-Language Models (VLMs) are highly effective in retrieving semantically relevant images. However, in practice, relevance alone is often insuffic...
Existing 3D facial-landmark methods localize points on visible skin, but whether CT-defined internal skeletal landmarks can be inferred from external ...
A flat training curve does not reveal whether a neural network has reached a global optimum, is locally trapped, is representation-limited, or is mism...
Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Cove...
Evaluating detailed image captions from Vision-Language Models (VLMs) requires going beyond surface-level semantic similarity. Reference-based metrics...
A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the m...
Deleting a gene token from a cell's input sequence offers a convenient native strategy for in silico perturbation, but the resulting embedding delta m...
Background. Designing high-quality Objective Structured Clinical Examination (OSCE) stations is a time-consuming process. Generative artificial intell...
Unmanned aerial vehicle (UAV) photogrammetry requires camera networks that provide sufficient surface coverage, image overlap, parallax, and resolutio...
Contrastive vision-language models such as CLIP and BLIP are typically trained on short image captions, limiting their ability to retrieve images from...
Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, f...
Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reus...
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools...