Zobrazeno 1 - 10
of 1 656
pro vyhledávání: '"Erickson,Bradley A."'
Autor:
Slavkova, Kalina P., Traughber, Melanie, Chen, Oliver, Bakos, Robert, Goldstein, Shayna, Harms, Dan, Erickson, Bradley J., Siddiqui, Khan M.
Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential o
Externí odkaz:
http://arxiv.org/abs/2411.17891
Autor:
Rouzrokh, Pouria, Khosravi, Bardia, Faghani, Shahriar, Mulford, Kellen L., Taunton, Michael J., Erickson, Bradley J., Wyles, Cody C.
Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few previous studies attempted to convert two or more input radiographs i
Externí odkaz:
http://arxiv.org/abs/2404.13000
Autor:
Rouzrokh, Pouria, Faghani, Shahriar, Gamble, Cooper U., Shariatnia, Moein, Erickson, Bradley J.
Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses. This mitigates hallucinations and allows for the
Externí odkaz:
http://arxiv.org/abs/2404.04287
Autor:
Sobek, Joseph, Inojosa, Jose R. Medina, Inojosa, Betsy J. Medina, Rassoulinejad-Mousavi, S. M., Conte, Gian Marco, Lopez-Jimenez, Francisco, Erickson, Bradley J.
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However,
Externí odkaz:
http://arxiv.org/abs/2312.07729
Autor:
Khosravi, Bardia, Li, Frank, Dapamede, Theo, Rouzrokh, Pouria, Gamble, Cooper U., Trivedi, Hari M., Wyles, Cody C., Sellergren, Andrew B., Purkayastha, Saptarshi, Erickson, Bradley J., Gichoya, Judy W.
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a prom
Externí odkaz:
http://arxiv.org/abs/2311.09402
Autor:
Rouzrokh, Pouria, Khosravi, Bardia, Faghani, Shahriar, Moassefi, Mana, Vahdati, Sanaz, Erickson, Bradley J.
Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models. The generation of synthetic data that might be fre
Externí odkaz:
http://arxiv.org/abs/2210.12113
Autor:
Khosravi, Bardia, Rouzrokh, Pouria, Erickson, Bradley J., Garner, Hillary W., Wenger, Doris E., Taunton, Michael J., Wyles, Cody C.
Publikováno v:
In Arthroplasty Today October 2024 29
Autor:
Singh, Yashbir, Faghani, Shahriar, Eaton, John E., Venkatesh, Sudhakar K., Erickson, Bradley J.
Publikováno v:
In Mayo Clinic Proceedings: Digital Health September 2024 2(3):470-476
Autor:
Ye, Run Zhou, Lipatov, Kirill, Diedrich, Daniel, Bhattacharyya, Anirban, Erickson, Bradley J., Pickering, Brian W., Herasevich, Vitaly
Publikováno v:
In Journal of Critical Care August 2024 82
Autor:
Sabov, Moldovan, Denic, Aleksandar, Mullan, Aidan F., Luehrs, Anthony C., Kline, Timothy L., Erickson, Bradley J., Potretzke, Theodora A., Thompson, R. Houston, Sharma, Vidit, Harris, Peter C., Rule, Andrew D.
Publikováno v:
In American Journal of Kidney Diseases July 2024 84(1):62-72