Latest AI and machine learning research in cultural competence for healthcare professionals.
Due to the scarcity of expert-annotated data, Semi-Supervised Medical Image Segmentation (SSMIS) has emerged as a promising approach. Many anatomical structures in medical images exhibit significant intra-class heterogeneity, with different regions showing heterogeneous intensity patterns within the same structure. However, existing methods inadequately exploit this intensity-manifested intra-clas...
Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies. To address these limitations, we present a novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world...
Objectives: To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest...
Codon usage bias is a fundamental genomic characteristic that prefers non-random preferential use of synonymous codons. It is a major determinant of t...
Dysregulation of post-translational modifications (PTMs) is associated with severe pathologies, including cancers and Alzheimer's disease. Despite the...
Precision medicine relies on accurate and generalizable predictions for patients across the spectrum of human diversity. Because capturing biological ...
Multi-modal image matching is essential for visual localization and multi-sensor fusion, but it is hindered by the scarcity of large-scale training da...
Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such...
The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance dispa...
Multimodal Large Language Models (MLLMs) incur prohibitive inference costs due to long visual token sequences. Training-free visual token reduction pr...
Deep learning models have advanced de novo peptide sequencing, but their predictions may reflect both physics-based spectral evidence and learned pept...
From protein structure prediction to novel protein generation, challenging protein engineering tasks have been made possible by advancements in machin...
State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multipl...
Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic ...
Subject-driven image generation faces an "Identity-Diversity Paradox", where strong identity preservation often leads to rigid and low-diversity outpu...
Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (VTE) risk stratification; however, reliable risk de...
Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While ...
Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated sa...
Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing resea...
Multimodal large language models (MLLMs) are increasingly deployed in personally and societally consequential settings, yet the visual cues that shape...