Latest AI and machine learning research in medical education for healthcare professionals.
We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviour to optimisation dynamics; we analyse curves of image reconstruction against latent representation quality, revealing trajectories that organise around two distinct regimes early in training. Models in the reconstruction regime prioritise image fide...
Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require models to predict or simulate multiple levels of dynamic constituents, such as actions taken in the visual sequence, and the associated changes to the visual environment that result. To address this challenge, we propose a dynamic schema-guided world mode...
Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LL...
Performance evaluation in AI systems commonly assumes that random dataset splits produce independent and identically distributed (i.i.d.) subsets. We ...
Retention in antiretroviral therapy care remains a major challenge in high-burden settings such as Malawi, where substantial loss to follow up undermi...
We present LINet (Linear Integration Network), a Multi-Stream Neural Network (MSNN) for RGB-D scene classification. Current multi-modal architectures ...
Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised lea...
Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers...
While text-guided image editing has made remarkable progress, it remains limited in structural portrait retouching. Textual descriptions struggle to c...
Change Detection (CD) aims to identify semantic or structural changes from nearly registered multi-temporal images. While recent advances in training ...
Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where acti...
Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinica...
Abstract Introduction: A clinician's initial assessment during the mental status examination (MSE) places substantial weight on a patient's general ap...
Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at ...
Reconstructing realistic, physically plausible garments from a single image remains a fundamental challenge. Template-free methods capture surface geo...
Vision-Language Models (VLMs) have revolutionized document parsing by enabling end-to-end mapping from images to structured text, imposing a significa...
Reconstructing realistic, physically plausible garments from a single image remains a fundamental challenge. Template-free methods capture surface geo...
Protein behavior inside cells is dominated by the crowded nature of the intracellular environment. Progress in structure determination of proteins and...
Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant ...
Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require cos...