Zobrazeno 1 - 10
of 544
pro vyhledávání: '"Farah, E."'
Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface electromyogra
Externí odkaz:
http://arxiv.org/abs/2408.02547
Publikováno v:
Scientific Reports 14 (2024) 22516
Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. While such approaches deliver promising results, they do not leverage associated patient or scan information collected within Electronic He
Externí odkaz:
http://arxiv.org/abs/2407.04449
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of uncertainty have largely been limited to applications of ad-hoc soluti
Externí odkaz:
http://arxiv.org/abs/2312.00794
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. Although such approaches deliver promising results, they do not take advantage of the associated patient or scan information colle
Externí odkaz:
https://doaj.org/article/f8f7b9d27cc94b60812525bda035cbf7
Autor:
Guerra-Manzanares, Alejandro, Lopez, L. Julian Lechuga, Maniatakos, Michail, Shamout, Farah E.
Publikováno v:
Trustworthy Machine Learning for Healthcare. TML4H 2023. Lecture Notes in Computer Science, vol 13932
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered al
Externí odkaz:
http://arxiv.org/abs/2303.15563
Unrecognized patient deterioration can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensi
Externí odkaz:
http://arxiv.org/abs/2210.05881
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously.
Externí odkaz:
http://arxiv.org/abs/2207.07027
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the current state-of-the-art deep learni
Externí odkaz:
http://arxiv.org/abs/2111.02710
Autor:
Stadnick, Benjamin, Witowski, Jan, Rajiv, Vishwaesh, Chłędowski, Jakub, Shamout, Farah E., Cho, Kyunghyun, Geras, Krzysztof J.
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed mode
Externí odkaz:
http://arxiv.org/abs/2108.04800
Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inference, these networks cannot predict an unseen disease class. Incorpo
Externí odkaz:
http://arxiv.org/abs/2107.06563