Despite recent advances in feed-forward 3D Gaussian Splatting, generalizable
3D reconstruction remains challenging, particularly in multi-view
correspondence modeling. Existing approaches face a fundamental trade-off:
explicit methods achieve geome... read more
PURPOSE: Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space. To gain a deeper understanding of this relationship, an image space ... read more
BACKGROUND: In emergency departments, residents and physicians interpret X-rays to identify fractures, with distal radius fractures being the most common in children. Skilled radiologists typically ensure accurate readings in well-resourced hospitals... read more
Dexterous grasp datasets are vital for embodied intelligence, but mostly
emphasize grasp stability, ignoring functional grasps needed for tasks like
opening bottle caps or holding cup handles. Most rely on bulky, costly, and
hard-to-control high-DO... read more
Synthesizing realistic and spatially precise anomalies is essential for
enhancing the robustness of industrial anomaly detection systems. While recent
diffusion-based methods have demonstrated strong capabilities in modeling
complex defect patterns... read more
Research on bias in Text-to-Image (T2I) models has primarily focused on
demographic representation and stereotypical attributes, overlooking a
fundamental question: how does grammatical gender influence visual
representation across languages? We in... read more
Cold-Start Active Learning (CSAL) aims to select informative samples for
annotation without prior knowledge, which is important for improving annotation
efficiency and model performance under a limited annotation budget in medical
image analysis. M... read more
X-ray medical report generation is one of the important applications of
artificial intelligence in healthcare. With the support of large foundation
models, the quality of medical report generation has significantly improved.
However, challenges suc... read more
Adversarial perturbations are useful tools for exposing vulnerabilities in
neural networks. Existing adversarial perturbation methods for object detection
are either limited to attacking CNN-based detectors or weak against
transformer-based detecto... read more
Multimodal medical image fusion integrates complementary information from
different imaging modalities to enhance diagnostic accuracy and treatment
planning. While deep learning methods have advanced performance, existing
approaches face critical l... read more
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.