Artificial Intelligence Medical Compendium

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

Showing 21,791 to 21,800 of 216,627 articles

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

arXiv May 7, 2026
Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are limited to image-level classification and lack clinically structured reporting. We propose Retina-RAG, ... read more 
Computer Vision and Pattern Recognition cs.AI

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

arXiv May 7, 2026
Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are limited to image-level classification and lack clinically structured reporting. We propose Retina-RAG, ... read more 
Computer Vision and Pattern Recognition cs.AI

Bridging visual saliency and large language models for explainable deep learning in medical imaging

arXiv May 7, 2026
The opaque nature of deep learning models remains a significant barrier to their clinical adoption in medical imaging. This paper presents a multimodal explainability framework that bridges the gap between convolutional neural network (CNN) predictio... read more 
Computer Vision and Pattern Recognition cs.LG

Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance

arXiv May 7, 2026
We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape and reflectance, with a unified spatial-angular structured light and a... read more 
Computer Vision and Pattern Recognition

Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant

arXiv May 7, 2026
Forward-Forward (FF) training allows each layer to learn from a local goodness criterion. In cumulative-goodness variants, however, later layers can inherit a task that earlier layers have already partially separated. We formalize this phenomenon as ... read more 
Machine Learning cs.AI

Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving

arXiv May 7, 2026
End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decisions and hard-to-localize risks. Existing methods either rely on auxiliary monitoring models or generat... read more 
Machine Learning

ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision

arXiv May 7, 2026
Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing solutions mainly co... read more 
Computer Vision and Pattern Recognition

Spark3R: Asymmetric Token Reduction Makes Fast Feed-Forward 3D Reconstruction

arXiv May 7, 2026
Feed-forward 3D reconstruction models based on Vision Transformers can directly estimate scene geometry and camera poses from a small set of input images, but scaling them to video inputs with hundreds or thousands of frames remains challenging due t... read more 
Computer Vision and Pattern Recognition

A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions

arXiv May 7, 2026
While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation from example data points remains a relatively underexplored and challenging problem. To this end, we prop... read more 
Machine Learning

When Labels Have Structure: Improving Image Classification with Hierarchy-Aware Cross-Entropy

arXiv May 7, 2026
Standard cross-entropy is the default classification loss across virtually all of machine learning, yet it treats all misclassifications equally, ignoring the semantic distances that a class hierarchy encodes. We propose Hierarchy-Aware Cross-Entropy... read more 
Machine Learning cs.CV
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