Zobrazeno 1 - 10
of 616
pro vyhledávání: '"Fuentes, David A"'
Autor:
Muthusivarajan, Rajarajeswari, Celaya, Adrian, Farhat, Maguy, Talpur, Wasif, Langshaw, Holly, White, Victoria, Elliott, Andrew, Thrower, Sara, Schellingerhout, Dawid, Fuentes, David, Chung, Caroline
Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tissue resulting from the surgical tumor resection. To develop a model fo
Externí odkaz:
http://arxiv.org/abs/2409.15177
Autor:
Twam, Awj, Jacobsen, Megan, Glenn, Rachel, Klopp, Ann, Venkatesan, Aradhana M., Fuentes, David
Cervical cancer remains the fourth most common malignancy amongst women worldwide.1 Concurrent chemoradiotherapy (CRT) serves as the mainstay definitive treatment regimen for locally advanced cervical cancers and includes external beam radiation foll
Externí odkaz:
http://arxiv.org/abs/2409.11456
Autor:
Amare, Rohan, Stolley, Danielle, Parrish, Steve, Jacobsen, Megan, Layman, Rick, Santos, Chimamanda, Riviere, Beatrice, Fowlkes, Natalie, Fuentes, David, Cressman, Erik
Objective: Innovative therapies such as thermoembolization are expected to play an important role in improvising care for patients with diseases such as hepatocellular carcinoma. Thermoembolization is a minimally invasive strategy that combines therm
Externí odkaz:
http://arxiv.org/abs/2409.06811
Autor:
Celaya, Adrian, Lim, Evan, Glenn, Rachel, Mi, Brayden, Balsells, Alex, Netherton, Tucker, Chung, Caroline, Riviere, Beatrice, Fuentes, David
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new methods mak
Externí odkaz:
http://arxiv.org/abs/2407.21343
Autor:
Reinhardt, Alec, Nikzad, Newsha, Hollis, Raven J., Jacobson, Galia, Roach, Millicent A., Badawy, Mohamed, Park, Peter Chul, Beretta, Laura, Jalal, Prasun K, Fuentes, David T., Koay, Eugene J., Kundu, Suprateek
Diagnostic imaging has gained prominence as potential biomarkers for early detection and diagnosis in a diverse array of disorders including cancer. However, existing methods routinely face challenges arising from various factors such as image hetero
Externí odkaz:
http://arxiv.org/abs/2403.07126
In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods like PINNs re
Externí odkaz:
http://arxiv.org/abs/2311.00259
Autor:
Wahid, Kareem A., Cardenas, Carlos E., Marquez, Barbara, Netherton, Tucker J., Kann, Benjamin H., Court, Laurence E., He, Renjie, Naser, Mohamed A., Moreno, Amy C., Fuller, Clifton D., Fuentes, David
Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-co
Externí odkaz:
http://arxiv.org/abs/2310.10867
Autor:
Nikzad, Newsha, Fuentes, David Thomas, Roach, Millicent, Chowdhury, Tasadduk, Cagley, Matthew, Badawy, Mohamed, Elkhesen, Ahmed, Hassan, Manal, Elsayes, Khaled, Beretta, Laura, Koay, Eugene Jon, Jalal, Prasun Kumar
Background and Aims: Limited methods exist to accurately characterize risk of malignant progression of liver lesions in patients undergoing surveillance for hepatocellular carcinoma (HCC). Enhancement pattern mapping (EPM) measures voxel-based root m
Externí odkaz:
http://arxiv.org/abs/2309.03980
In this paper, we propose a distributed Generative Adversarial Networks (discGANs) to generate synthetic tabular data specific to the healthcare domain. While using GANs to generate images has been well studied, little to no attention has been given
Externí odkaz:
http://arxiv.org/abs/2304.04290
Accurate medical imaging segmentation is critical for precise and effective medical interventions. However, despite the success of convolutional neural networks (CNNs) in medical image segmentation, they still face challenges in handling fine-scale f
Externí odkaz:
http://arxiv.org/abs/2304.02725