Zobrazeno 1 - 10
of 734
pro vyhledávání: '"Höbel, 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:
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:
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:
Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 1 (2022)
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e
Externí odkaz:
http://arxiv.org/abs/2112.10074
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
Deep learning has the potential to automate many clinically useful tasks in medical imaging. However translation of deep learning into clinical practice has been hindered by issues such as lack of the transparency and interpretability in these "black
Externí odkaz:
http://arxiv.org/abs/2109.04392
As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively detect and evaluate subgroup disparities to ensure accountability and
Externí odkaz:
http://arxiv.org/abs/2107.02716
Autor:
Gupta, Sharut, Singh, Praveer, Chang, Ken, Qu, Liangqiong, Aggarwal, Mehak, Arun, Nishanth, Vaswani, Ashwin, Raghavan, Shruti, Agarwal, Vibha, Gidwani, Mishka, Hoebel, Katharina, Patel, Jay, Lu, Charles, Bridge, Christopher P., Rubin, Daniel L., Kalpathy-Cramer, Jayashree
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant decline in performance when tested at other institutions. While pooli
Externí odkaz:
http://arxiv.org/abs/2103.13511
Autor:
Gupta, Sharut, Singh, Praveer, Chang, Ken, Aggarwal, Mehak, Arun, Nishanth, Qu, Liangqiong, Hoebel, Katharina, Patel, Jay, Gidwani, Mishka, Vaswani, Ashwin, Rubin, Daniel L, Kalpathy-Cramer, Jayashree
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types. While simply training on
Externí odkaz:
http://arxiv.org/abs/2011.08096
Autor:
Aggarwal, Mehak, Arun, Nishanth, Gupta, Sharut, Vaswani, Ashwin, Chen, Bryan, Li, Matthew, Chang, Ken, Patel, Jay, Hoebel, Katherine, Gidwani, Mishka, Kalpathy-Cramer, Jayashree, Singh, Praveer
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision maki
Externí odkaz:
http://arxiv.org/abs/2011.07482