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Joseph R. Ledsam
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2020 – today
- 2024
- [i8]Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg:
Predicting Generalization of AI Colonoscopy Models to Unseen Data. CoRR abs/2403.09920 (2024) - 2023
- [c2]Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Jennifer N. Wei, Zelda E. Mariet, Poomarin Phloyphisut, Hidetoshi Shimokawa, Joseph R. Ledsam, Lucy J. Colwell, Akihiro Imura:
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions. NeurIPS 2023 - [i7]Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Jennifer N. Wei, Zelda Mariet, Poomarin Phloyphisut, Hidetoshi Shimokawa, Joseph R. Ledsam, Lucy J. Colwell, Akihiro Imura:
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions. CoRR abs/2306.03329 (2023) - [i6]Joel Shor, Hiro-o Yamano, Daisuke Tsurumaru, Yotam Intrator, Hiroki Kayama, Joseph R. Ledsam, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Eiji Oki, Roman Goldenberg, Ehud Rivlin, Ichiro Takemasa:
The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries. CoRR abs/2312.06833 (2023) - 2021
- [j1]Sercan Ö. Arik, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - 2020
- [i5]Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, A. Taylan Cemgil, S. M. Ali Eslami, Olaf Ronneberger:
Contrastive Training for Improved Out-of-Distribution Detection. CoRR abs/2007.05566 (2020)
2010 – 2019
- 2018
- [c1]Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger:
A Probabilistic U-Net for Segmentation of Ambiguous Images. NeurIPS 2018: 6965-6975 - [i4]Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger:
A Probabilistic U-Net for Segmentation of Ambiguous Images. CoRR abs/1806.05034 (2018) - [i3]Stanislav Nikolov, Sam Blackwell, Ruheena Mendes, Jeffrey De Fauw, Clemens Meyer, Cían Hughes, Harry Askham, Bernardino Romera-Paredes, Alan Karthikesalingam, Carlton Chu, Dawn Carnell, Cheng Boon, Derek D'Souza, Syed Ali Moinuddin, Kevin Sullivan, DeepMind Radiographer Consortium, Hugh Montgomery, Geraint Rees, Ricky Sharma, Mustafa Suleyman, Trevor Back, Joseph R. Ledsam, Olaf Ronneberger:
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. CoRR abs/1809.04430 (2018) - 2016
- [i2]Jeffrey De Fauw, Pearse A. Keane, Nenad Tomasev, Daniel Visentin, George van den Driessche, Mike Johnson, Cían Hughes, Carlton Chu, Joseph R. Ledsam, Trevor Back, Tunde Peto, Geraint Rees, Hugh Montgomery, Rosalind Raine, Olaf Ronneberger, Julien Cornebise:
Automated analysis of retinal imaging using machine learning techniques for computer vision. F1000Research 5: 1573 (2016) - [i1]Carlton Chu, Jeffrey De Fauw, Nenad Tomasev, Bernardino Romera-Paredes, Cían Hughes, Joseph R. Ledsam, Trevor Back, Hugh Montgomery, Geraint Rees, Rosalind Raine, Kevin Sullivan, Syed Ali Moinuddin, Derek D'Souza, Olaf Ronneberger, Ruheena Mendes, Julien Cornebise:
Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans. F1000Research 5: 2104 (2016)
Coauthor Index
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last updated on 2024-08-05 21:11 CEST by the dblp team
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