Artificial Intelligence Medical Compendium

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

Showing 61,761 to 61,770 of 229,091 articles

Visual question answering-based image-finding generation for pulmonary nodules on chest CT from structured annotations

arXiv Jan 16, 2026
Interpretation of imaging findings based on morphological characteristics is important for diagnosing pulmonary nodules on chest computed tomography (CT) images. In this study, we constructed a visual question answering (VQA) dataset from structured ... read more 
Image and Video Processing cs.CV physics.med-ph

Conformal Point and the Calibrated Conic

arXiv Jan 16, 2026
This gives some information about the conformal point and the calibrating conic, and their relationship one to the other. These concepts are useful for visualizing image geometry, and lead to intuitive ways to compute geometry, such as angles and dir... read more 
Computer Vision and Pattern Recognition

IDDR-NGP: Incorporating Detectors for Distractor Removal with Instant Neural Radiance Field

arXiv Jan 16, 2026
This paper presents the first unified distractor removal method, named IDDR-NGP, which directly operates on Instant-NPG. The method is able to remove a wide range of distractors in 3D scenes, such as snowflakes, confetti, defoliation and petals, wher... read more 
Computer Vision and Pattern Recognition cs.AI

AVP-Pro: An Adaptive Multi-Modal Fusion and Contrastive Learning Approach for Comprehensive Two-Stage Antiviral Peptide Identification

arXiv Jan 16, 2026
The accurate identification of antiviral peptides (AVPs) is crucial for novel drug development. However, existing methods still have limitations in capturing complex sequence dependencies and distinguishing confusing samples with high similarity. To ... read more 
Machine Learning

Matching High-Dimensional Geometric Quantiles for Test-Time Adaptation of Transformers and Convolutional Networks Alike

arXiv Jan 16, 2026
Test-time adaptation (TTA) refers to adapting a classifier for the test data when the probability distribution of the test data slightly differs from that of the training data of the model. To the best of our knowledge, most of the existing TTA appro... read more 
Machine Learning cs.CV

KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure

arXiv Jan 16, 2026
Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identifying their neural signatures remains challenging. We introduced KOCOBrain: KuramotO COupled Brain Gr... read more 
Neurons and Cognition cs.CV

Generating metamers of human scene understanding

arXiv Jan 16, 2026
Human vision combines low-resolution "gist" information from the visual periphery with sparse but high-resolution information from fixated locations to construct a coherent understanding of a visual scene. In this paper, we introduce MetamerGen, a to... read more 
Computer Vision and Pattern Recognition cs.AI

Pigment Network Detection and Classification in Dermoscopic Images Using Directional Imaging Algorithms and Convolutional Neural Networks

arXiv Jan 16, 2026
Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typ... read more 
Image and Video Processing cs.AI cs.CV

Depression Detection Based on Electroencephalography Using a Hybrid Deep Neural Network CNN-GRU and MRMR Feature Selection

arXiv Jan 16, 2026
This study investigates the detection and classification of depressive and non-depressive states using deep learning approaches. Depression is a prevalent mental health disorder that substantially affects quality of life, and early diagnosis can grea... read more 
Quantitative Methods cs.LG

A Confidence-Variance Theory for Pseudo-Label Selection in Semi-Supervised Learning

arXiv Jan 16, 2026
Most pseudo-label selection strategies in semi-supervised learning rely on fixed confidence thresholds, implicitly assuming that prediction confidence reliably indicates correctness. In practice, deep networks are often overconfident: high-confidence... read more 
Machine Learning cs.AI
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