Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Bridge, Christopher P"'
Autor:
Gonçalves, Tiago, Pulido-Arias, Dagoberto, Willett, Julian, Hoebel, Katharina V., Cleveland, Mason, Ahmed, Syed Rakin, Gerstner, Elizabeth, Kalpathy-Cramer, Jayashree, Cardoso, Jaime S., Bridge, Christopher P., Kim, Albert E.
The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor a
Externí odkaz:
http://arxiv.org/abs/2404.16397
Autor:
Krishnaswamy, Deepa, Kovács, Bálint, Denner, Stefan, Pieper, Steve, Clunie, David, Bridge, Christopher P., Kapur, Tina, Maier-Hein, Klaus H., Fedorov, Andrey
With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata p
Externí odkaz:
http://arxiv.org/abs/2404.10892
Autor:
Tripathi, Satvik, Mutter, Liam, Muppuri, Meghana, Dheer, Suhani, Garza-Frias, Emiliano, Awan, Komal, Jha, Aakash, Dezube, Michael, Tabari, Azadeh, Bridge, Christopher P., Daye, Dania
This study introduces and evaluates the PRECISE framework, utilizing OpenAI's GPT-4 to enhance patient engagement by providing clearer and more accessible chest X-ray reports at a sixth-grade reading level. The framework was tested on 500 reports, de
Externí odkaz:
http://arxiv.org/abs/2403.00788
Autor:
Dorfner, Felix J., Jürgensen, Liv, Donle, Leonhard, Mohamad, Fares Al, Bodenmann, Tobias R., Cleveland, Mason C., Busch, Felix, Adams, Lisa C., Sato, James, Schultz, Thomas, Kim, Albert E., Merkow, Jameson, Bressem, Keno K., Bridge, Christopher P.
Introduction: With the rapid advances in large language models (LLMs), there have been numerous new open source as well as commercial models. While recent publications have explored GPT-4 in its application to extracting information of interest from
Externí odkaz:
http://arxiv.org/abs/2402.12298
Autor:
Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, Fedorov, Andrey
Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-ba
Externí odkaz:
http://arxiv.org/abs/2306.00150
Autor:
Hoebel, Katharina V., Lemay, Andreanne, Campbell, John Peter, Ostmo, Susan, Chiang, Michael F., Bridge, Christopher P., Li, Matthew D., Singh, Praveer, Coyner, Aaron S., Kalpathy-Cramer, Jayashree
Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordin
Externí odkaz:
http://arxiv.org/abs/2305.19097
Autor:
Gupta, Vikash, Erdal, Barbaros Selnur, Ramirez, Carolina, Floca, Ralf, Jackson, Laurence, Genereaux, Brad, Bryson, Sidney, Bridge, Christopher P, Kleesiek, Jens, Nensa, Felix, Braren, Rickmer, Younis, Khaled, Penzkofer, Tobias, Bucher, Andreas Michael, Qin, Ming Melvin, Bae, Gigon, Lee, Hyeonhoon, Cardoso, M. Jorge, Ourselin, Sebastien, Kerfoot, Eric, Choudhury, Rahul, White, Richard D., Cook, Tessa, Bericat, David, Lungren, Matthew, Haukioja, Risto, Shuaib, Haris
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between
Externí odkaz:
http://arxiv.org/abs/2212.14177
Autor:
Lemay, Andreanne, Hoebel, Katharina, Bridge, Christopher P., Befano, Brian, De Sanjosé, Silvia, Egemen, Diden, Rodriguez, Ana Cecilia, Schiffman, Mark, Campbell, John Peter, Kalpathy-Cramer, Jayashree
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models output predictions with low variation during independent tests carried out
Externí odkaz:
http://arxiv.org/abs/2202.07562
Autor:
Dasegowda, Giridhar, Sato, James Yuichi, Elton, Daniel C., Garza-Frias, Emiliano, Schultz, Thomas, Bridge, Christopher P., Bizzo, Bernardo C., Kalra, Mannudeep K., Dreyer, Keith J.
Publikováno v:
In Clinical Imaging August 2024 112
Autor:
Lemay, Andreanne, Hoebel, Katharina, Bridge, Christopher P., Egemen, Didem, Rodriguez, Ana Cecilia, Schiffman, Mark, Campbell, John Peter, Kalpathy-Cramer, Jayashree
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is given to classification performance without assessing the model repeatab
Externí odkaz:
http://arxiv.org/abs/2111.06754