no code implementations • 3 Feb 2023 • Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
1 code implementation • 1 Jul 2022 • Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt, Tong Yu, Aditya Murali, Luca Sestini, Chinedu Innocent Nwoye, Idris Hamoud, Saurav Sharma, Antoine Fleurentin, Georgios Exarchakis, Alexandros Karargyris, Nicolas Padoy
Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7. 4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%.
Ranked #1 on Semantic Segmentation on Endoscapes
1 code implementation • 3 Jun 2022 • Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
no code implementations • 14 Mar 2022 • Hasan Kassem, Deepak Alapatt, Pietro Mascagni, AI4SafeChole Consortium, Alexandros Karargyris, Nicolas Padoy
With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition.
1 code implementation • 29 Sep 2021 • Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, Peter Mattson
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
1 code implementation • 12 Apr 2021 • Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
1 code implementation • 26 Feb 2021 • Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, Spyridon Bakas
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
1 code implementation • 15 Sep 2020 • Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy Wu, Arjun Sharma, Matthew Tong, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth A. Krupinski, Mehdi Moradi
We report deep learning experiments that utilize the attention maps produced by eye gaze dataset to show the potential utility of this data.
no code implementations • 4 Aug 2020 • Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu, Alexandros Karargyris, Mehdi Moradi
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
no code implementations • 2 Aug 2020 • Satyananda Kashyap, Alexandros Karargyris, Joy Wu, Yaniv Gur, Arjun Sharma, Ken C. L. Wong, Mehdi Moradi, Tanveer Syeda-Mahmood
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging.
no code implementations • 27 Jul 2020 • Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur, Joy T. Wu, Ashutosh Jadhav, Satyananda Kashyap, Alexandros Karargyris, Anup Pillai, Arjun Sharma, Ali Bin Syed, Orest Boyko, Mehdi Moradi
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.
no code implementations • 3 May 2020 • Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.
no code implementations • 21 Jun 2019 • Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari, Yaniv Gur, Satyananda Kashyap, Alexandros Karargyris, Mehdi Moradi, Anup Pillai, Karthik Sheshadri, Wei-Ting Wang, Ken C. L. Wong, Joy T. Wu
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals.
no code implementations • 21 Jun 2019 • Alexandros Karargyris, Ken C. L. Wong, Joy T. Wu, Mehdi Moradi, Tanveer Syeda-Mahmood
We experiment with two different deep neural network architectures and show that the proposed method results in a large improvement in the performance of the classifiers, specially on negated findings.
no code implementations • 9 Mar 2019 • Alexandros Karargyris, Satyananda Kashyap, Joy T. Wu, Arjun Sharma, Mehdi Moradi, Tanveer Syeda-Mahmood
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades.
no code implementations • 7 May 2018 • Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood, Mehdi Moradi
We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier.
no code implementations • 31 Oct 2015 • Alexandros Karargyris
Given a large dataset of colored images the color space transformation module tries to learn color space transformations that increase overall classification accuracy.