Large language models for time series: A survey
Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …
language processing and computer vision. Going beyond text, image and graphics, LLMs …
Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems
Recent advancements have seen Large Language Models (LLMs) and Large Multimodal
Models (LMMs) surpassing general human capabilities in various tasks, approaching the …
Models (LMMs) surpassing general human capabilities in various tasks, approaching the …
Large language model-informed ECG dual attention network for heart failure risk prediction
Heart failure (HF) poses a significant public health challenge, with a rising global mortality
rate. Early detection and prevention of HF could significantly reduce its impact. We introduce …
rate. Early detection and prevention of HF could significantly reduce its impact. We introduce …
OSGAN: Omni-scale and Global-aware ECG arrhythmia diagnostic network
Automated arrhythmia detection using electrocardiogram (ECG) signals is critical for
cardiovascular disease prevention and treatment. However, the widely used CNN-based …
cardiovascular disease prevention and treatment. However, the widely used CNN-based …
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
Multimodal summarization with multimodal output (MSMO) has emerged as a promising
research direction. Nonetheless numerous limitations exist within existing public MSMO …
research direction. Nonetheless numerous limitations exist within existing public MSMO …
Transfer learning with clinical concept embeddings from large language models
Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple
clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer …
clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer …
ECGBERT: Understanding hidden language of ECGs with self-supervised representation learning
In the medical field, current ECG signal analysis approaches rely on supervised deep neural
networks trained for specific tasks that require substantial amounts of labeled data …
networks trained for specific tasks that require substantial amounts of labeled data …
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning
A Bleich, A Linnemann, BH Diem… - arxiv preprint arxiv …, 2024 - arxiv.org
Recent advances in deep learning and natural language generation have significantly
improved image captioning, enabling automated, human-like descriptions for visual content …
improved image captioning, enabling automated, human-like descriptions for visual content …
Cardiac disease diagnosis on imbalanced electrocardiography data through optimal transport augmentation
In this paper, we focus on a new method of data augmentation to solve the data imbalance
problem within imbalanced ECG datasets to improve the robustness and accuracy of heart …
problem within imbalanced ECG datasets to improve the robustness and accuracy of heart …
ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
The success of Multimodal Large Language Models (MLLMs) in the medical auxiliary field
shows great potential, allowing patients to engage in conversations using physiological …
shows great potential, allowing patients to engage in conversations using physiological …