Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Although DINOv3 has demonstrated remarkable semantic discrimination in natural imagery, its direct application to volumetric medical segmentation is hindered by inherent dimension and domain disparities. To resolve these issues, we propose DINO-Med3D, a two-stage progressive framework that repurpose the pre-trained DINOv3 encoder for 3D medical tasks. In the first stage, we mitigate the dimension ...
Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data. However, existing approaches operate predominantly via visual pattern matching, relying heavily on pixel-level similarities. This visual-centric dependency often falters in clinical scenarios char...
Precise biomedical image segmentation is crucial for clinical diagnosis. Geometric cues (e.g., boundary, shape, and topology) can improve structural c...
Accurate pediatric brain tumor segmentation remains challenging due to limited annotated data, heterogeneous imaging phenotypes, diffuse tumor boundar...
Accurate and robust classification of medical pathology images is pivotal for computer-aided diagnosis. However, the deployment of deep learning model...
Medical image segmentation is often framed as a search for stronger architectures, but this can obscure a more fundamental question: what does the dat...
Static neural point reconstructions capture a subject at high fidelity from posed images. Given such a reconstruction, we aim to animate it to follow ...
Background: Long-acting pre-exposure prophylaxis (PrEP) expands HIV prevention options for women. However, PrEP impact depends on addressing persisten...
Background.Renoprotective therapies - SGLT2 inhibitors, finerenone, and renin-angiotensin system inhibitors (RASi) - remain underutilisedin chronic ki...
Background and Purpose: Drug resistant epilepsy (DRE) affects approximately 15 million people worldwide, and surgery remains the only curative option....
Large language models are typically evaluated under fixed instruction contexts, implicitly treating correct refusal as a stable model property. We sho...
Background: Professionalism and effective communication are foundational determinants of patient safety and quality of care. Unprofessional behaviors ...
Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures an...
Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and ...
Few-Shot Medical Image Segmentation (FSMIS) aims to delineate novel anatomical targets from one or a few annotated support images, addressing the anno...
LED Virtual Production (VP) uses large LED volumes to render backgrounds in real time, enabling in-camera visual effects but making post-shot changes ...
Background: Obesity significantly increases the risk of prognosis and clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). While research on ...
Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domai...
Medical image segmentation remains challenging in low-data regimes, where scarce annotations often yield poor generalization and ambiguous boundaries ...
Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ...