Peer-reviewed papers, preprints, and conference publications.
Flanagan ME, et al.
Abstract Time series forecasting has applications across domains and industries, especially in healthcare, but the technical expertise required to analyze data, build models, and interpret results can be a barrier…
Abstract This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make customized large language models (LLMs) more accessible. By capitalizing on…
Abstract U-Net-based deep learning models have garnered significant attention in recent years due to their strong denoising capabilities in image restoration tasks. This study critically evaluates both the strengths and…
Abstract Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a…
Flanagan ME, Gutman D, Dugger BN, Cooper L, Pearce TM, et al.
Abstract Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. This separation prevents models from learning representations that…
Abstract Histopathological image analysis plays a critical role in modern medical diagnostics, particularly in the detection and classification of various types of cancer. This study proposes a method called HistoDARE…
Abstract Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce Vision Foundry, a code-free, HIPAA-compliant platform that democratizes pre-training, adaptation,…
Abstract Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research, requiring experts to extract often complex information from unstructured electronic health record (EHR) narratives. We present…