Zobrazeno 1 - 10
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pro vyhledávání: '"BROWN, DONALD E."'
Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domai
Externí odkaz:
http://arxiv.org/abs/2410.16485
Autor:
Moradinasab, Nazanin, Shankman, Laura S., Deaton, Rebecca A., Owens, Gary K., Brown, Donald E.
Domain adaptive semantic segmentation aims to generate accurate and dense predictions for an unlabeled target domain by leveraging a supervised model trained on a labeled source domain. The prevalent self-training approach involves retraining the den
Externí odkaz:
http://arxiv.org/abs/2406.19225
Autor:
Blanks, Zachary, Brown, Donald E.
Quantifying the complexity and irregularity of time series data is a primary pursuit across various data-scientific disciplines. Sample entropy (SampEn) is a widely adopted metric for this purpose, but its reliability is sensitive to the choice of it
Externí odkaz:
http://arxiv.org/abs/2405.06112
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
Autor:
Sengupta, Saurav, Brown, Donald E.
Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current state-of-the-art (SOTA) methods has been a challenge due to the high resol
Externí odkaz:
http://arxiv.org/abs/2312.01435
Autor:
Sengupta, Saurav, Brown, Donald E.
Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current state-of-the-art methods has been a challenge due to the high resolution o
Externí odkaz:
http://arxiv.org/abs/2311.06176
Autor:
Moradinasab, Nazanin, Deaton, Rebecca A., Shankman, Laura S., Owens, Gary K., Brown, Donald E.
Recently, deep learning-based methods achieved promising performance in nuclei detection and classification applications. However, training deep learning-based methods requires a large amount of pixel-wise annotated data, which is time-consuming and
Externí odkaz:
http://arxiv.org/abs/2309.03744
Autor:
Sengupta, Saurav, Loomba, Johanna, Sharma, Suchetha, Brown, Donald E., Thorpe, Lorna, Haendel, Melissa A, Chute, Christopher G, Hong, Stephanie
Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab
Externí odkaz:
http://arxiv.org/abs/2210.02490
Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial
Externí odkaz:
http://arxiv.org/abs/2208.00098
In this work, we propose a mutual information (MI) based unsupervised domain adaptation (UDA) method for the cross-domain nuclei segmentation. Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in
Externí odkaz:
http://arxiv.org/abs/2206.14437
Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary. With rapid advancements in tec
Externí odkaz:
http://arxiv.org/abs/2204.12432