Artificial Intelligence Medical Compendium

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

Showing 63,641 to 63,650 of 230,801 articles

Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment

arXiv Jan 15, 2026
Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intellig... read more 
Machine Learning cs.CV eess.IV q-bio.QM

Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection

arXiv Jan 15, 2026
Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widespread, it is increasingly imperati... read more 
Machine Learning

Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications

arXiv Jan 15, 2026
The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effec... read more 
Machine Learning

Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks

arXiv Jan 15, 2026
In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance of dataset distillation. Deep neural networks achieve remarkable perfo... read more 
Computer Vision and Pattern Recognition cs.AI

LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data

arXiv Jan 15, 2026
Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a res... read more 
Machine Learning cs.AI

V-Zero: Self-Improving Multimodal Reasoning with Zero Annotation

arXiv Jan 15, 2026
Recent advances in multimodal learning have significantly enhanced the reasoning capabilities of vision-language models (VLMs). However, state-of-the-art approaches rely heavily on large-scale human-annotated datasets, which are costly and time-consu... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG

Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text

arXiv Jan 15, 2026
Multimodal models excel in English, supported by abundant image-text and audio-text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely heavily on machine translation, whil... read more 
Machine Learning

Enhancing Visual In-Context Learning by Multi-Faceted Fusion

arXiv Jan 15, 2026
Visual In-Context Learning (VICL) has emerged as a powerful paradigm, enabling models to perform novel visual tasks by learning from in-context examples. The dominant "retrieve-then-prompt" approach typically relies on selecting the single best visua... read more 
Computer Vision and Pattern Recognition

Beyond Single Prompts: Synergistic Fusion and Arrangement for VICL

arXiv Jan 15, 2026
Vision In-Context Learning (VICL) enables inpainting models to quickly adapt to new visual tasks from only a few prompts. However, existing methods suffer from two key issues: (1) selecting only the most similar prompt discards complementary cues fro... read more 
Computer Vision and Pattern Recognition

VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation

arXiv Jan 15, 2026
Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which i... read more 
Computer Vision and Pattern Recognition
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