Search Results - "Ledsam, Joseph R"

Effective gene expression prediction from sequence by integrating long-range interactions by Avsec, Žiga, Agarwal, Vikram, Visentin, Daniel, Ledsam, Joseph R., Grabska-Barwinska, Agnieszka, Taylor, Kyle R., Assael, Yannis, Jumper, John, Kohli, Pushmeet, Kelley, David R.

“…How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend…”
Get full text

International evaluation of an AI system for breast cancer screening by McKinney, Scott Mayer, Sieniek, Marcin, Godbole, Varun, Godwin, Jonathan, Antropova, Natasha, Ashrafian, Hutan, Back, Trevor, Chesus, Mary, Corrado, Greg S., Darzi, Ara, Etemadi, Mozziyar, Garcia-Vicente, Florencia, Gilbert, Fiona J., Halling-Brown, Mark, Hassabis, Demis, Jansen, Sunny, Karthikesalingam, Alan, Kelly, Christopher J., King, Dominic, Ledsam, Joseph R., Melnick, David, Mostofi, Hormuz, Peng, Lily, Reicher, Joshua Jay, Romera-Paredes, Bernardino, Sidebottom, Richard, Suleyman, Mustafa, Tse, Daniel, Young, Kenneth C., De Fauw, Jeffrey, Shetty, Shravya

“…Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful 1 . Despite the existence of…”
Get full text

Clinically applicable deep learning for diagnosis and referral in retinal disease by De Fauw, Jeffrey, Ledsam, Joseph R., Romera-Paredes, Bernardino, Nikolov, Stanislav, Tomasev, Nenad, Blackwell, Sam, Askham, Harry, Glorot, Xavier, O’Donoghue, Brendan, Visentin, Daniel, van den Driessche, George, Lakshminarayanan, Balaji, Meyer, Clemens, Mackinder, Faith, Bouton, Simon, Ayoub, Kareem, Chopra, Reena, King, Dominic, Karthikesalingam, Alan, Hughes, Cían O., Raine, Rosalind, Hughes, Julian, Sim, Dawn A., Egan, Catherine, Tufail, Adnan, Montgomery, Hugh, Hassabis, Demis, Rees, Geraint, Back, Trevor, Khaw, Peng T., Suleyman, Mustafa, Cornebise, Julien, Keane, Pearse A., Ronneberger, Olaf

“…The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial…”
Get full text

A clinically applicable approach to continuous prediction of future acute kidney injury by Tomašev, Nenad, Glorot, Xavier, Rae, Jack W., Zielinski, Michal, Askham, Harry, Saraiva, Andre, Mottram, Anne, Meyer, Clemens, Ravuri, Suman, Protsyuk, Ivan, Connell, Alistair, Hughes, Cían O., Karthikesalingam, Alan, Cornebise, Julien, Montgomery, Hugh, Rees, Geraint, Laing, Chris, Baker, Clifton R., Peterson, Kelly, Reeves, Ruth, Hassabis, Demis, King, Dominic, Suleyman, Mustafa, Back, Trevor, Nielson, Christopher, Ledsam, Joseph R., Mohamed, Shakir

“…The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a…”
Get full text

Predicting conversion to wet age-related macular degeneration using deep learning by Yim, Jason, Chopra, Reena, Spitz, Terry, Winkens, Jim, Obika, Annette, Kelly, Christopher, Askham, Harry, Lukic, Marko, Huemer, Josef, Fasler, Katrin, Moraes, Gabriella, Meyer, Clemens, Wilson, Marc, Dixon, Jonathan, Hughes, Cian, Rees, Geraint, Khaw, Peng T., Karthikesalingam, Alan, King, Dominic, Hassabis, Demis, Suleyman, Mustafa, Back, Trevor, Ledsam, Joseph R., Keane, Pearse A., De Fauw, Jeffrey

“…Progression to exudative ‘wet’ age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye,…”
Get full text

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis by Liu, Xiaoxuan, Faes, Livia, Kale, Aditya U, Wagner, Siegfried K, Fu, Dun Jack, Bruynseels, Alice, Mahendiran, Thushika, Moraes, Gabriella, Shamdas, Mohith, Kern, Christoph, Ledsam, Joseph R, Schmid, Martin K, Balaskas, Konstantinos, Topol, Eric J, Bachmann, Lucas M, Keane, Pearse A, Denniston, Alastair K

“…Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care…”
Get full text

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning by Varadarajan, Avinash V., Bavishi, Pinal, Ruamviboonsuk, Paisan, Chotcomwongse, Peranut, Venugopalan, Subhashini, Narayanaswamy, Arunachalam, Cuadros, Jorge, Kanai, Kuniyoshi, Bresnick, George, Tadarati, Mongkol, Silpa-archa, Sukhum, Limwattanayingyong, Jirawut, Nganthavee, Variya, Ledsam, Joseph R., Keane, Pearse A., Corrado, Greg S., Peng, Lily, Webster, Dale R.

“…Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by…”
Get full text

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study by Nikolov, Stanislav, Blackwell, Sam, Zverovitch, Alexei, Mendes, Ruheena, Livne, Michelle, De Fauw, Jeffrey, Patel, Yojan, Meyer, Clemens, Askham, Harry, Romera-Paredes, Bernadino, Kelly, Christopher, Karthikesalingam, Alan, Chu, Carlton, Carnell, Dawn, Boon, Cheng, D'Souza, Derek, Moinuddin, Syed Ali, Garie, Bethany, McQuinlan, Yasmin, Ireland, Sarah, Hampton, Kiarna, Fuller, Krystle, Montgomery, Hugh, Rees, Geraint, Suleyman, Mustafa, Back, Trevor, Hughes, Cían Owen, Ledsam, Joseph R, Ronneberger, Olaf

“…Background: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for…”
Get full text

Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study by Faes, Livia, Wagner, Siegfried K, Fu, Dun Jack, Liu, Xiaoxuan, Korot, Edward, Ledsam, Joseph R, Back, Trevor, Chopra, Reena, Pontikos, Nikolas, Kern, Christoph, Moraes, Gabriella, Schmid, Martin K, Sim, Dawn, Balaskas, Konstantinos, Bachmann, Lucas M, Denniston, Alastair K, Keane, Pearse A

“…Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of…”
Get full text

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan by Arık, Sercan Ö., Shor, Joel, Sinha, Rajarishi, Yoon, Jinsung, Ledsam, Joseph R., Le, Long T., Dusenberry, Michael W., Yoder, Nathanael C., Popendorf, Kris, Epshteyn, Arkady, Euphrosine, Johan, Kanal, Elli, Jones, Isaac, Li, Chun-Liang, Luan, Beth, Mckenna, Joe, Menon, Vikas, Singh, Shashank, Sun, Mimi, Ravi, Ashwin Sura, Zhang, Leyou, Sava, Dario, Cunningham, Kane, Kayama, Hiroki, Tsai, Thomas, Yoneoka, Daisuke, Nomura, Shuhei, Miyata, Hiroaki, Pfister, Tomas

“…The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that…”
Get full text

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records by Tomašev, Nenad, Harris, Natalie, Baur, Sebastien, Mottram, Anne, Glorot, Xavier, Rae, Jack W., Zielinski, Michal, Askham, Harry, Saraiva, Andre, Magliulo, Valerio, Meyer, Clemens, Ravuri, Suman, Protsyuk, Ivan, Connell, Alistair, Hughes, Cían O., Karthikesalingam, Alan, Cornebise, Julien, Montgomery, Hugh, Rees, Geraint, Laing, Chris, Baker, Clifton R., Osborne, Thomas F., Reeves, Ruth, Hassabis, Demis, King, Dominic, Suleyman, Mustafa, Back, Trevor, Nielson, Christopher, Seneviratne, Martin G., Ledsam, Joseph R., Mohamed, Shakir

“…Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk…”
Get full text

Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Shor, Joel, McNeil, Carson, Intrator, Yotam, Ledsam, Joseph R., Yamano, Hiro-o, Tsurumaru, Daisuke, Kayama, Hiroki, Hamabe, Atsushi, Ando, Koji, Ota, Mitsuhiko, Ogino, Haruei, Nakase, Hiroshi, Kobayashi, Kaho, Miyo, Masaaki, Oki, Eiji, Takemasa, Ichiro, Rivlin, Ehud, Goldenberg, Roman

“…Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on…”
Get full text

Addendum: International evaluation of an AI system for breast cancer screening by McKinney, Scott Mayer, Sieniek, Marcin, Godbole, Varun, Godwin, Jonathan, Antropova, Natasha, Ashrafian, Hutan, Back, Trevor, Chesus, Mary, Corrado, Greg S., Darzi, Ara, Etemadi, Mozziyar, Garcia-Vicente, Florencia, Gilbert, Fiona J., Halling-Brown, Mark, Hassabis, Demis, Jansen, Sunny, Karthikesalingam, Alan, Kelly, Christopher J., King, Dominic, Ledsam, Joseph R., Melnick, David, Mostofi, Hormuz, Peng, Lily, Reicher, Joshua Jay, Romera-Paredes, Bernardino, Sidebottom, Richard, Suleyman, Mustafa, Tse, Daniel, Young, Kenneth C., De Fauw, Jeffrey, Shetty, Shravya

Get full text

Modeling DCE-MRI at low temporal resolution: A case study on rheumatoid arthritis by Ledsam, Joseph R., Hodgson, Richard, Moots, Robert J., Sourbron, Steven P.

“…Purpose To identify the optimal tracer‐kinetic modeling strategy for dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) data acquired at low…”
Get full text

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan by Kiraly, Atilla P, Cunningham, Corbin A, Najafi, Ryan, Nabulsi, Zaid, Yang, Jie, Lau, Charles, Ledsam, Joseph R, Ye, Wenxing, Ardila, Diego, McKinney, Scott M, Pilgrim, Rory, Liu, Yun, Saito, Hiroaki, Shimamura, Yasuteru, Etemadi, Mozziyar, Melnick, David, Jansen, Sunny, Corrado, Greg S, Peng, Lily, Tse, Daniel, Shetty, Shravya, Prabhakara, Shruthi, Nadich, David P, Beladia, Neeral, Eswaran, Krish

“…Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and…”
Get full text

Artificial intelligence as a second reader for screening mammography by Nakai, Etsuji, Miyagi, Yumi, Suzuki, Kazuhiro, Scoccia Pappagallo, Alessandro, Kayama, Hiroki, Matsuba, Takehito, Yang, Lin, Xu, Shawn, Kelly, Christopher, Najafi, Ryan, Kohlberger, Timo, Golden, Daniel, Uddin, Akib, Nakamura, Yusuke, Kokubu, Yumi, Takahashi, Yoko, Ueno, Takayuki, Oguchi, Masahiko, Ohno, Shinji, Ledsam, Joseph R

Get full text

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions by Tsuruta, Hirofumi, Yamazaki, Hiroyuki, Maeda, Ryota, Tamura, Ryotaro, Wei, Jennifer N, Mariet, Zelda, Phloyphisut, Poomarin, Shimokawa, Hidetoshi, Ledsam, Joseph R, Colwell, Lucy, Imura, Akihiro

“…Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods,…”
Get full text

Predicting Generalization of AI Colonoscopy Models to Unseen Data by Shor, Joel, McNeil, Carson, Intrator, Yotam, Ledsam, Joseph R, Yamano, Hiro-o, Tsurumaru, Daisuke, Kayama, Hiroki, Hamabe, Atsushi, Ando, Koji, Ota, Mitsuhiko, Ogino, Haruei, Nakase, Hiroshi, Kobayashi, Kaho, Miyo, Masaaki, Oki, Eiji, Takemasa, Ichiro, Rivlin, Ehud, Goldenberg, Roman

“…$\textbf{Background}$: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for…”
Get full text

Contrastive Training for Improved Out-of-Distribution Detection by Winkens, Jim, Bunel, Rudy, Roy, Abhijit Guha, Stanforth, Robert, Natarajan, Vivek, Ledsam, Joseph R, MacWilliams, Patricia, Kohli, Pushmeet, Karthikesalingam, Alan, Kohl, Simon, Cemgil, Taylan, Eslami, S. M. Ali, Ronneberger, Olaf

“…Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper…”
Get full text

A Probabilistic U-Net for Segmentation of Ambiguous Images by Kohl, Simon A. A, Romera-Paredes, Bernardino, Meyer, Clemens, De Fauw, Jeffrey, Ledsam, Joseph R, Maier-Hein, Klaus H, Eslami, S. M. Ali, Rezende, Danilo Jimenez, Ronneberger, Olaf

“…Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which…”
Get full text