Zobrazeno 1 - 10
of 3 995
pro vyhledávání: '"Şahiner, A."'
Autor:
Kim, Andrea, Saharkhiz, Niloufar, Sizikova, Elena, Lago, Miguel, Sahiner, Berkman, Delfino, Jana, Badano, Aldo
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in pa
Externí odkaz:
http://arxiv.org/abs/2408.00191
Autor:
Sizikova, Elena, Badal, Andreu, Delfino, Jana G., Lago, Miguel, Nelson, Brandon, Saharkhiz, Niloufar, Sahiner, Berkman, Zamzmi, Ghada, Badano, Aldo
A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdens
Externí odkaz:
http://arxiv.org/abs/2407.01561
Autor:
Mukherjee, Subrata, Coroller, Thibaud, Wang, Craig, Samala, Ravi K., Hu, Tingting, Gokcay, Didem, Petrick, Nicholas, Sahiner, Berkman, Cao, Qian
Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess thes
Externí odkaz:
http://arxiv.org/abs/2404.16544
Autor:
Monod, Mélodie, Krusche, Peter, Cao, Qian, Sahiner, Berkman, Petrick, Nicholas, Ohlssen, David, Coroller, Thibaud
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific parametric forms, TorchSurv enables the use of custom PyTorch-based deep su
Externí odkaz:
http://arxiv.org/abs/2404.10761
Autor:
Zamzmi, Ghada, Venkatesh, Kesavan, Nelson, Brandon, Prathapan, Smriti, Yi, Paul H., Sahiner, Berkman, Delfino, Jana G.
Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical settings, where data drift may cause unexpected performance that jeopardiz
Externí odkaz:
http://arxiv.org/abs/2402.08088
Autor:
Feng, Jean, Subbaswamy, Adarsh, Gossmann, Alexej, Singh, Harvineet, Sahiner, Berkman, Kim, Mi-Ok, Pennello, Gene, Petrick, Nicholas, Pirracchio, Romain, Xia, Fan
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algorithm interacts with its environment, the algorithm can affect the data-
Externí odkaz:
http://arxiv.org/abs/2311.11463
Autor:
Sizikova, Elena, Saharkhiz, Niloufar, Sharma, Diksha, Lago, Miguel, Sahiner, Berkman, Delfino, Jana G., Badano, Aldo
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available. We propose an evaluat
Externí odkaz:
http://arxiv.org/abs/2310.18494
Autor:
Feng, Jean, Gossmann, Alexej, Pirracchio, Romain, Petrick, Nicholas, Pennello, Gene, Sahiner, Berkman
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous populations and satisfy strong notions of algorithmic fairness. Howeve
Externí odkaz:
http://arxiv.org/abs/2307.15247
Autor:
Sahiner, Pervin, Utkualp, Nevin
Publikováno v:
African Journal of Reproductive Health / La Revue Africaine de la Santé Reproductive, 2023 Nov 01. 27(11), 18-25.
Externí odkaz:
https://www.jstor.org/stable/27275569
Autor:
Whitney, Heather M., Baughan, Natalie, Myers, Kyle J., Drukker, Karen, Gichoya, Judy, Bower, Brad, Chen, Weijie, Gruszauskas, Nicholas, Kalpathy-Cramer, Jayashree, Koyejo, Sanmi, Sá, Rui C., Sahiner, Berkman, Zhang, Zi, Giger, Maryellen L.
Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify lon
Externí odkaz:
http://arxiv.org/abs/2303.10501