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pro vyhledávání: '"Clifton, David A"'
Autor:
Saha, Pramit, Wagner, Felix, Mishra, Divyanshu, Peng, Can, Thakur, Anshul, Clifton, David, Kamnitsas, Konstantinos, Noble, J. Alison
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors
Externí odkaz:
http://arxiv.org/abs/2411.11912
Large language models (LLMs) are emerging as promising tools for mental health care, offering scalable support through their ability to generate human-like responses. However, the effectiveness of these models in clinical settings remains unclear. Th
Externí odkaz:
http://arxiv.org/abs/2408.11288
Autor:
Anibal, James, Gunkel, Jasmine, Awan, Shaheen, Huth, Hannah, Nguyen, Hang, Le, Tram, Bélisle-Pipon, Jean-Christophe, Boyer, Micah, Hazen, Lindsey, Consortium, Bridge2AI Voice, Bensoussan, Yael, Clifton, David, Wood, Bradford
Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient
Externí odkaz:
http://arxiv.org/abs/2408.07896
Utilizing large pre-trained models for specific tasks has yielded impressive results. However, fully fine-tuning these increasingly large models is becoming prohibitively resource-intensive. This has led to a focus on more parameter-efficient transfe
Externí odkaz:
http://arxiv.org/abs/2408.02421
Autor:
Gowda, Shreyank N, Clifton, David A.
The Segment Anything Model (SAM) has achieved remarkable successes in the realm of natural image segmentation, but its deployment in the medical imaging sphere has encountered challenges. Specifically, the model struggles with medical images that fea
Externí odkaz:
http://arxiv.org/abs/2408.00181
Autor:
Gowda, Shreyank N, Clifton, David A.
Contemporary medical contrastive learning faces challenges from inconsistent semantics and sample pair morphology, leading to dispersed and converging semantic shifts. The variability in text reports, due to multiple authors, complicates semantic con
Externí odkaz:
http://arxiv.org/abs/2407.16264
Autor:
Wentzel, Andrew, Attia, Serageldin, Zhang, Xinhua, Canahuate, Guadalupe, Fuller, Clifton David, Marai, G. Elisabeta
Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk
Externí odkaz:
http://arxiv.org/abs/2407.13107
Autor:
Rohanian, Omid, Nouriborji, Mohammadmahdi, Seminog, Olena, Furst, Rodrigo, Mendy, Thomas, Levanita, Shanthi, Kadri-Alabi, Zaharat, Jabin, Nusrat, Toale, Daniela, Humphreys, Georgina, Antonio, Emilia, Bucher, Adrian, Norton, Alice, Clifton, David A.
This paper introduces the Pandemic PACT Advanced Categorisation Engine (PPACE) along with its associated dataset. PPACE is a fine-tuned model developed to automatically classify research abstracts from funded biomedical projects according to WHO-alig
Externí odkaz:
http://arxiv.org/abs/2407.10086
Autor:
Xing, Xingrun, Gao, Boyan, Zhang, Zheng, Clifton, David A., Xiao, Shitao, Du, Li, Li, Guoqi, Zhang, Jiajun
The recent advancements in large language models (LLMs) with billions of parameters have significantly boosted their performance across various real-world applications. However, the inference processes for these models require substantial energy and
Externí odkaz:
http://arxiv.org/abs/2407.04752
Autor:
Zhou, Rushuang, Clifton, Lei, Liu, Zijun, Chan, Kannie W. Y., Clifton, David A., Zhang, Yuan-Ting, Dong, Yining
The label scarcity problem is the main challenge that hinders the wide application of deep learning systems in automatic cardiovascular diseases (CVDs) detection using electrocardiography (ECG). Tuning pre-trained models alleviates this problem by tr
Externí odkaz:
http://arxiv.org/abs/2406.14377