Search Results - "Muti, Hannah Sophie"

The future landscape of large language models in medicine by Clusmann, Jan, Kolbinger, Fiona R., Muti, Hannah Sophie, Carrero, Zunamys I., Eckardt, Jan-Niklas, Laleh, Narmin Ghaffari, Löffler, Chiara Maria Lavinia, Schwarzkopf, Sophie-Caroline, Unger, Michaela, Veldhuizen, Gregory P., Wagner, Sophia J., Kather, Jakob Nikolas

“…Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public…”
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Pan-cancer image-based detection of clinically actionable genetic alterations by Kather, Jakob Nikolas, Heij, Lara R, Grabsch, Heike I, Loeffler, Chiara, Echle, Amelie, Muti, Hannah Sophie, Krause, Jeremias, Niehues, Jan M, Sommer, Kai A J, Bankhead, Peter, Kooreman, Loes F S, Schulte, Jefree J, Cipriani, Nicole A, Buelow, Roman D, Boor, Peter, Ortiz-Brüchle, Nadi-Na, Hanby, Andrew M, Speirs, Valerie, Kochanny, Sara, Patnaik, Akash, Srisuwananukorn, Andrew, Brenner, Hermann, Hoffmeister, Michael, van den Brandt, Piet A, Jäger, Dirk, Trautwein, Christian, Pearson, Alexander T, Luedde, Tom

“…Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are…”
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Classical mathematical models for prediction of response to chemotherapy and immunotherapy by Ghaffari Laleh, Narmin, Loeffler, Chiara Maria Lavinia, Grajek, Julia, Staňková, Kateřina, Pearson, Alexander T, Muti, Hannah Sophie, Trautwein, Christian, Enderling, Heiko, Poleszczuk, Jan, Kather, Jakob Nikolas

“…Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and…”
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Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study by Muti, Hannah Sophie, Heij, Lara Rosaline, Keller, Gisela, Kohlruss, Meike, Langer, Rupert, Dislich, Bastian, Cheong, Jae-Ho, Kim, Young-Woo, Kim, Hyunki, Kook, Myeong-Cherl, Cunningham, David, Allum, William H, Langley, Ruth E, Nankivell, Matthew G, Quirke, Philip, Hayden, Jeremy D, West, Nicholas P, Irvine, Andrew J, Yoshikawa, Takaki, Oshima, Takashi, Huss, Ralf, Grosser, Bianca, Roviello, Franco, d'Ignazio, Alessia, Quaas, Alexander, Alakus, Hakan, Tan, Xiuxiang, Pearson, Alexander T, Luedde, Tom, Ebert, Matthias P, Jäger, Dirk, Trautwein, Christian, Gaisa, Nadine Therese, Grabsch, Heike I, Kather, Jakob Nikolas

“…Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV)…”
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Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types by Loeffler, Chiara Maria Lavinia, El Nahhas, Omar S M, Muti, Hannah Sophie, Carrero, Zunamys I, Seibel, Tobias, van Treeck, Marko, Cifci, Didem, Gustav, Marco, Bretz, Kevin, Gaisa, Nadine T, Lehmann, Kjong-Van, Leary, Alexandra, Selenica, Pier, Reis-Filho, Jorge S, Ortiz-Bruechle, Nadina, Kather, Jakob Nikolas

“…Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with…”
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Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning by Saldanha, Oliver Lester, Muti, Hannah Sophie, Grabsch, Heike I., Langer, Rupert, Dislich, Bastian, Kohlruss, Meike, Keller, Gisela, van Treeck, Marko, Hewitt, Katherine Jane, Kolbinger, Fiona R., Veldhuizen, Gregory Patrick, Boor, Peter, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

“…Background Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve…”
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Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types by Loeffler, Chiara Maria Lavinia, Gaisa, Nadine T, Muti, Hannah Sophie, van Treeck, Marko, Echle, Amelie, Ghaffari Laleh, Narmin, Trautwein, Christian, Heij, Lara R, Grabsch, Heike I, Ortiz Bruechle, Nadina, Kather, Jakob Nikolas

“…In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology…”
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Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study by Veldhuizen, Gregory Patrick, Röcken, Christoph, Behrens, Hans-Michael, Cifci, Didem, Muti, Hannah Sophie, Yoshikawa, Takaki, Arai, Tomio, Oshima, Takashi, Tan, Patrick, Ebert, Matthias P., Pearson, Alexander T., Calderaro, Julien, Grabsch, Heike I., Kather, Jakob Nikolas

“…Introduction The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver…”
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Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology by Ghaffari Laleh, Narmin, Muti, Hannah Sophie, Loeffler, Chiara Maria Lavinia, Echle, Amelie, Saldanha, Oliver Lester, Mahmood, Faisal, Lu, Ming Y., Trautwein, Christian, Langer, Rupert, Dislich, Bastian, Buelow, Roman D., Grabsch, Heike Irmgard, Brenner, Hermann, Chang-Claude, Jenny, Alwers, Elizabeth, Brinker, Titus J., Khader, Firas, Truhn, Daniel, Gaisa, Nadine T., Boor, Peter, Hoffmeister, Michael, Schulz, Volkmar, Kather, Jakob Nikolas

“…•We provide a large-scale benchmarking comparison of multiple Deep Learning approaches for analysis of pathology slides in multiple large patient cohorts.•HIA,…”
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Swarm learning for decentralized artificial intelligence in cancer histopathology by Saldanha, Oliver Lester, Quirke, Philip, West, Nicholas P., James, Jacqueline A., Loughrey, Maurice B., Grabsch, Heike I., Salto-Tellez, Manuel, Alwers, Elizabeth, Cifci, Didem, Ghaffari Laleh, Narmin, Seibel, Tobias, Gray, Richard, Hutchins, Gordon G. A., Brenner, Hermann, van Treeck, Marko, Yuan, Tanwei, Brinker, Titus J., Chang-Claude, Jenny, Khader, Firas, Schuppert, Andreas, Luedde, Tom, Trautwein, Christian, Muti, Hannah Sophie, Foersch, Sebastian, Hoffmeister, Michael, Truhn, Daniel, Kather, Jakob Nikolas

“…Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems…”
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Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance by Niehues, Jan M., Müller-Franzes, Gustav, Schirris, Yoni, Wagner, Sophia Janine, Jendrusch, Michael, Kloor, Matthias, Pearson, Alexander T., Muti, Hannah Sophie, Hewitt, Katherine J., Veldhuizen, Gregory P., Zigutyte, Laura, Truhn, Daniel, Kather, Jakob Nikolas

“…Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation method, outperforming previous Generative Adversarial Networks (GANs) in…”
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Erratum to ‘Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology’ Medical Image Analysis, Volume 79, July 2022, 102474 by Ghaffari Laleh, Narmin, Muti, Hannah Sophie, Loeffler, Chiara Maria Lavinia, Echle, Amelie, Saldanha, Oliver Lester, Mahmood, Faisal, Lu, Ming Y., Trautwein, Christian, Langer, Rupert, Dislich, Bastian, Buelow, Roman D., Grabsch, Heike Irmgard, Brenner, Hermann, Chang-Claude, Jenny, Alwers, Elizabeth, Brinker, Titus J., Khader, Firas, Truhn, Daniel, Gaisa, Nadine T., Boor, Peter, Hoffmeister, Michael, Schulz, Volkmar, Kather, Jakob Nikolas

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End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study by Jiang, Xiaofeng, Hoffmeister, Michael, Brenner, Hermann, Muti, Hannah Sophie, Yuan, Tanwei, Foersch, Sebastian, West, Nicholas P, Brobeil, Alexander, Jonnagaddala, Jitendra, Hawkins, Nicholas, Ward, Robyn L, Brinker, Titus J, Saldanha, Oliver Lester, Ke, Jia, Müller, Wolfram, Grabsch, Heike I, Quirke, Philip, Truhn, Daniel, Kather, Jakob Nikolas

“…Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological…”
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Direct image to subtype prediction for brain tumors using deep learning by Hewitt, Katherine J, Löffler, Chiara M L, Muti, Hannah Sophie, Berghoff, Anna Sophie, Eisenlöffel, Christian, van Treeck, Marko, Carrero, Zunamys I, El Nahhas, Omar S M, Veldhuizen, Gregory P, Weil, Sophie, Saldanha, Oliver Lester, Bejan, Laura, Millner, Thomas O, Brandner, Sebastian, Brückmann, Sascha, Kather, Jakob Nikolas

“…Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health…”
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Author Correction: Pan-cancer image-based detection of clinically actionable genetic alterations by Kather, Jakob Nikolas, Heij, Lara R, Grabsch, Heike I, Loeffler, Chiara, Echle, Amelie, Muti, Hannah Sophie, Krause, Jeremias, Niehues, Jan M, Sommer, Kai A J, Bankhead, Peter, Kooreman, Loes F S, Schulte, Jefree J, Cipriani, Nicole A, Buelow, Roman D, Boor, Peter, Ortiz-Brüchle, Nadina, Hanby, Andrew M, Speirs, Valerie, Kochanny, Sara, Patnaik, Akash, Srisuwananukorn, Andrew, Brenner, Hermann, Hoffmeister, Michael, van den Brandt, Piet A, Jäger, Dirk, Trautwein, Christian, Pearson, Alexander T, Luedde, Tom

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Benchmarking foundation models as feature extractors for weakly-supervised computational pathology by Neidlinger, Peter, Nahhas, Omar S. M. El, Muti, Hannah Sophie, Lenz, Tim, Hoffmeister, Michael, Brenner, Hermann, van Treeck, Marko, Langer, Rupert, Dislich, Bastian, Behrens, Hans Michael, Röcken, Christoph, Foersch, Sebastian, Truhn, Daniel, Marra, Antonio, Saldanha, Oliver Lester, Kather, Jakob Nikolas

“…Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant…”
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