AIMC Topic: Datasets as Topic

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Deep Learning-Based Synthetic Skin Lesion Image Classification.

Studies in health technology and informatics
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study propose...

From data to diagnosis: skin cancer image datasets for artificial intelligence.

Clinical and experimental dermatology
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. Thi...

Development and external validation of deep learning clinical prediction models using variable-length time series data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected...

Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes.

Multimodal learning for temporal relation extraction in clinical texts.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, par...

What predicts citation counts and translational impact in headache research? A machine learning analysis.

Cephalalgia : an international journal of headache
BACKGROUND: We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive.

Sex classification from functional brain connectivity: Generalization to multiple datasets.

Human brain mapping
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear wh...