Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Large language models are typically evaluated under fixed instruction contexts, implicitly treating ...
Background: Professionalism and effective communication are foundational determinants of patient saf...
Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabli...
Decision trees partition the feature space using hard binary thresholds, assigning identical confide...
Few-Shot Medical Image Segmentation (FSMIS) aims to delineate novel anatomical targets from one or a...
LED Virtual Production (VP) uses large LED volumes to render backgrounds in real time, enabling in-c...
Background: Obesity significantly increases the risk of prognosis and clinical outcomes in pancreati...
Recent advances in generative models have empowered impressive layered image generation, yet their s...
Medical image segmentation remains challenging in low-data regimes, where scarce annotations often y...
Carotid atherosclerosis is a major contributor to ischemic stroke and transient ischemic attack. Con...
In large-scale visual recognition and data mining tasks, the presence of noisy labels severely under...
Monocular depth foundation models generalize well across scenes, yet they are typically optimized wi...
Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-l...
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its ...
Hyperspectral image classification demands spatially coherent predictions and precise boundary delin...
The scarcity of high-quality annotated medical data, particularly in mental health, poses a signific...
Semi-supervised learning (SSL) has become a promising solution to alleviate the annotation burden of...
Urban environments contain many imaging sensors built for specific purposes, including ATM, body-wor...
Graph neural networks achieve strong node-classification accuracy, but their learned message passing...
The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image...
Flow matching has recently emerged as a principled framework for learning continuous-time transport ...