Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 42,581 to 42,590 of 223,853 articles

Prostate Cancer Screening in the MR Imaging Era: Evidence, Strategies, and Future Directions.

Radiologic clinics of North America Mar 11, 2026
Prostate cancer screening has entered a new era with the integration of MR imaging and artificial intelligence (AI) into diagnostic workflows. This paradigm shift allows for the more accurate identification of clinically significant prostate cancer w... read more 

What do near-optimal learning rate schedules look like?

arXiv Mar 11, 2026
A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor in the success or failure of the training process, but beyond having s... read more 
Machine Learning

Fuel Gauge: Estimating Chain-of-Thought Length Ahead of Time in Large Multimodal Models

arXiv Mar 11, 2026
Reasoning Large Multi-modality Models (LMMs) have become the de facto choice for many applications. However, these models rely on a Chain-of-Thought (CoT) process that is lengthy and unpredictable at runtime, often resulting in inefficient use of com... read more 
Computer Vision and Pattern Recognition

EmoStory: Emotion-Aware Story Generation

arXiv Mar 11, 2026
Story generation aims to produce image sequences that depict coherent narratives while maintaining subject consistency across frames. Although existing methods have excelled in producing coherent and expressive stories, they remain largely emotion-ne... read more 
Computer Vision and Pattern Recognition

StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

arXiv Mar 11, 2026
Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints ... read more 
Computer Vision and Pattern Recognition

StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

arXiv Mar 11, 2026
Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics, causing uncontrollable stylization; 2) reliance on extra constraints ... read more 
Computer Vision and Pattern Recognition

One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination

arXiv Mar 11, 2026
Current training-free methods tackle MLLM hallucination with separate strategies: either enhancing visual signals or suppressing text inertia. However, these separate methods are insufficient due to critical trade-offs: simply enhancing vision often ... read more 
Computer Vision and Pattern Recognition

Frames2Residual: Spatiotemporal Decoupling for Self-Supervised Video Denoising

arXiv Mar 11, 2026
Self-supervised video denoising methods typically extend image-based frameworks into the temporal dimension, yet they often struggle to integrate inter-frame temporal consistency with intra-frame spatial specificity. Existing Video Blind-Spot Network... read more 
Computer Vision and Pattern Recognition

Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models

arXiv Mar 11, 2026
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering content via text prompts. However, many undesired outputs, such as an indivi... read more 
Machine Learning cs.CV

Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models

arXiv Mar 11, 2026
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering content via text prompts. However, many undesired outputs, such as an indivi... read more 
Machine Learning cs.CV
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