Clinically applicable deep learning for diagnosis and referral in retinal disease von 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

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A clinically applicable approach to continuous prediction of future acute kidney injury von 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

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Predicting conversion to wet age-related macular degeneration using deep learning von 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

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study von 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

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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records von 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

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Predicting conversion to wet age-related macular degeneration using deep learning von 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

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3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING von ASKHAM, Harry, NIKOLOV, Stanislav, ROMERA-PAREDES, Bernardino, MEYER, Clemens, BACK, Trevor, HUGHES, Cian, DE FAUW, Jeffrey, LEDSAM, Joseph R, BLACKWELL, Samuel, RONNEBERGER, Olaf

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PREDICTION OF FUTURE ADVERSE HEALTH EVENTS USING NEURAL NETWORKS BY PRE-PROCESSING INPUT SEQUENCES TO INCLUDE PRESENCE FEATURES von MOTTRAM, Anne, NOBRE DOS SANTOS, Andre Saraiva, MEYER, Clemens Ludwig, TOMASEV, Nenad, BACK, Trevor, ASKHAM, Harry, GLOROT, Xavier, ZIELINSKI, Michal, MOHAMED, Shakir, RAVURI, Suman, RAE, Jack William, LEDSAM, Joseph R, PROTSYUK, Ivan

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3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning von Meyer, Clemens Ludwig, Askham, Harry, Hughes, Cian, Ledsam, Joseph R, Romera-Paredes, Bernardino, De Fauw, Jeffrey, Nikolov, Stanislav, Blackwell, Samuel, Ronneberger, Olaf, Back, Trevor

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Generalizable medical image analysis using segmentation and classification neural networks von Askham, Harry, Suleyman, Mustafa, Ledsam, Joseph R, Romera-Paredes, Bernardino, Nikolov, Stanislav, Blackwell, Samuel, Glorot, Xavier, Ronneberger, Olaf, Lakshminarayanan, Balaji, Cornebise, Julien Robert Michel, Tomasev, Nenad, De Fauw, Jeffrey, Keane, Pearse A, Back, Trevor

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Predicting conversion to wet age-related macular degeneration using deep learning von Yim, Jason, Chopra, Reena, Spitz, Terry, Winkens, Jim, Obika, Annette, Kelly, Christopher, Askham, Harry

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Predicting conversion to wet age-related macular degeneration using deep learning von Yim, Jason, Chopra, Reena, Spitz, Terry, Winkens, Jim, Obika, Annette, Kelly, Christopher, Askham, Harry

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3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING von Meyer, Clemens Ludwig, Askham, Harry, Hughes, Cian, Ledsam, Joseph R, Romera-Paredes, Bernardino, De Fauw, Jeffrey, Nikolov, Stanislav, Blackwell, Samuel, Ronneberger, Olaf, Back, Trevor

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3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning von Meyer, Clemens Ludwig, Askham, Harry, Hughes, Cian, Ledsam, Joseph R, Romera-Paredes, Bernardino, De Fauw, Jeffrey, Nikolov, Stanislav, Blackwell, Samuel, Ronneberger, Olaf, Back, Trevor

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A clinically applicable approach to continuous prediction of future acute kidney injury von Tomasev, Nenad, Glorot, Xavier, Rae, Jack W, Zielinski, Michal, Askham, Harry, Saraiva, Andre, Mottram, Anne

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A clinically applicable approach to continuous prediction of future acute kidney injury von Tomasev, Nenad, Glorot, Xavier, Rae, Jack W, Zielinski, Michal, Askham, Harry, Saraiva, Andre, Mottram, Anne

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3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING von ASKHAM, Harry, NIKOLOV, Stanislav, ROMERA-PAREDES, Bernardino, MEYER, Clemens, BACK, Trevor, HUGHES, Cian, DE FAUW, Jeffrey, LEDSAM, Joseph R, BLACKWELL, Samuel, RONNEBERGER, Olaf

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Prediction of future adverse health events using neural networks by pre-processing input sequences to include presence features von Meyer, Clemens Ludwig, Mottram, Anne, Askham, Harry, Zielinski, Michal, Ledsam, Joseph R, Rae, Jack William, Ravuri, Suman, Glorot, Xavier, Mohamed, Shakir, Nobre Dos Santos, Andre Saraiva, Tomasev, Nenad, Protsyuk, Ivan, Back, Trevor

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Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy von Nikolov, Stanislav, Blackwell, Sam, Zverovitch, Alexei, Mendes, Ruheena, Livne, Michelle, De Fauw, Jeffrey, Patel, Yojan, Meyer, Clemens, Askham, Harry, Romera-Paredes, Bernardino, 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, Ledsam, Joseph R, Ronneberger, Olaf

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PREDICTION OF FUTURE ADVERSE HEALTH EVENTS USING NEURAL NETWORKS BY PRE-PROCESSING INPUT SEQUENCES TO INCLUDE PRESENCE FEATURES von MOTTRAM, Anne, NOBRE DOS SANTOS, Andre Saraiva, MEYER, Clemens Ludwig, TOMASEV, Nenad, BACK, Trevor, ASKHAM, Harry, GLOROT, Xavier, ZIELINSKI, Michal, MOHAMED, Shakir, RAVURI, Suman, RAE, Jack William, LEDSAM, Joseph R, PROTSYUK, Ivan

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