Zobrazeno 1 - 10
of 72
pro vyhledávání: '"SAEED, AAQIB"'
Pediatric sleep is an important but often overlooked area in health informatics. We present PedSleepMAE, a generative model that fully leverages multimodal pediatric sleep signals including multichannel EEGs, respiratory signals, EOGs and EMG. This m
Externí odkaz:
http://arxiv.org/abs/2411.00718
Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural language. The scarcity of labeled ECG data coupled with the diverse nature of
Externí odkaz:
http://arxiv.org/abs/2410.14464
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultations and auscultation, offering vital support in this area. However, the
Externí odkaz:
http://arxiv.org/abs/2410.05361
Accurate interpretation of Electrocardiogram (ECG) signals is pivotal for diagnosing cardiovascular diseases. Integrating ECG signals with their accompanying textual reports holds immense potential to enhance clinical diagnostics through the combinat
Externí odkaz:
http://arxiv.org/abs/2410.02131
Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retri
Externí odkaz:
http://arxiv.org/abs/2409.08788
Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentralized. However, accounting for the quality of data contributed by local
Externí odkaz:
http://arxiv.org/abs/2409.02189
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7782-7790
Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from eac
Externí odkaz:
http://arxiv.org/abs/2406.12658
Autor:
Spathis, Dimitris, Saeed, Aaqib, Etemad, Ali, Tonekaboni, Sana, Laskaridis, Stefanos, Deldari, Shohreh, Tang, Chi Ian, Schwab, Patrick, Tailor, Shyam
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.
Externí odkaz:
http://arxiv.org/abs/2403.10561
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive communication costs due to repe
Externí odkaz:
http://arxiv.org/abs/2401.14211
Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the vide
Externí odkaz:
http://arxiv.org/abs/2401.14107