Regression-based Deep-Learning predicts molecular biomarkers from pathology slides von El Nahhas, Omar S. M., Loeffler, Chiara M. L., Carrero, Zunamys I., van Treeck, Marko, Kolbinger, Fiona R., Hewitt, Katherine J., Muti, Hannah S., Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S., Kather, Jakob Nikolas

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In-context learning enables multimodal large language models to classify cancer pathology images von Ferber, Dyke, Wölflein, Georg, Wiest, Isabella C., Ligero, Marta, Sainath, Srividhya, Ghaffari Laleh, Narmin, El Nahhas, Omar S. M., Müller-Franzes, Gustav, Jäger, Dirk, Truhn, Daniel, Kather, Jakob Nikolas

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Author Correction: Regression-based Deep-Learning predicts molecular biomarkers from pathology slides von El Nahhas, Omar S. M., Loeffler, Chiara M. L., Carrero, Zunamys I., van Treeck, Marko, Kolbinger, Fiona R., Hewitt, Katherine J., Muti, Hannah S., Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S., Kather, Jakob Nikolas

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Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types von 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

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From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology von El Nahhas, Omar S M, van Treeck, Marko, Wölflein, Georg, Unger, Michaela, Ligero, Marta, Lenz, Tim, Wagner, Sophia J, Hewitt, Katherine J, Khader, Firas, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

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Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression von Ligero, Marta, Serna, Garazi, El Nahhas, Omar S M, Sansano, Irene, Mauchanski, Siarhei, Viaplana, Cristina, Calderaro, Julien, Toledo, Rodrigo A, Dienstmann, Rodrigo, Vanguri, Rami S, Sauter, Jennifer L, Sanchez-Vega, Francisco, Shah, Sohrab P, Ramón Y Cajal, Santiago, Garralda, Elena, Nuciforo, Paolo, Perez-Lopez, Raquel, Kather, Jakob Nikolas

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Direct image to subtype prediction for brain tumors using deep learning von 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

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Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study von Zeng, Qinghe, Klein, Christophe, Caruso, Stefano, Maille, Pascale, Allende, Daniela S, Mínguez, Beatriz, Ningarhari, Massih, Casadei-Gardini, Andrea, Rimini, Margherita, Perbellini, Riccardo, Boulagnon-Rombi, Camille, Rela, Mohamed, Vij, Mukul, Baulande, Sylvain, Lameiras, Sonia, Allende, Daniela S, Amaddeo, Giuliana, Argemi, Josepmaria, Beaufrère, Aurélie, Bermúdez-Ramos, María, Boulagnon-Rombi, Camille, Boursier, Jérôme, Calderaro, Julien, Campani, Claudia, Caruso, Stefano, Casadei-Gardini, Andrea, Castano Garcia, Andres, Chan, Stephen Lam, Di Tommaso, Luca, Digklia, Antonia, Garcia-Porrero, Guillermo, Ghaffari Laleh, Narmin, Gopal, Purva, Graham, Rondell P., Iavarone, Massimo, Iñarrairaegui, Mercedes, Kather, Jakob Nikolas, Klein, Christophe, Labgaa, Ismail, Lameiras, Sonia, Legoix, Patricia, Lequoy, Marie, Leung, Howard Ho-Wai, Loménie, Nicolas, Maggioni, Marco, Maille, Pascale, Marín-Zuluaga, Juan Ignacio, Mendoza-Pacas, Guillermo, Mínguez, Beatriz, El Nahhas, Omar S M, Nault, Jean-Charles, Ningarhari, Massih, Paradis, Valérie, Park, Young Nyun, Pawlotsky, Jean-Michel, Pedica, Federica, Perbellini, Riccardo, Pinato, David James, Pinter, Matthias, Radu, Pompilia, Regnault, Hélène, Reig, Maria, Rela, Mohamed, Rhee, Hyungjin, Rimassa, Lorenza, Sangro, Bruno, Scheiner, Bernhard, Sempoux, Christine, Su, Tung-Hung, Torres, Callie, Trépo, Eric, Varela, Maria, Verset, Gontran, Vij, Mukul, Vogel, Arndt, Zeng, Qinghe, Ziol, Marianne, Bruges, Léa, Gnemmi, Viviane, Nault, Jean-Charles, Campani, Claudia, Rhee, Hyungjin, Park, Young Nyun, Garcia-Porrero, Guillermo, Argemi, Josepmaria, Sangro, Bruno, D'Alessio, Antonio, Scheiner, Bernhard, Pinato, David James, Pinter, Matthias, Paradis, Valérie, Beaufrère, Aurélie, Peter, Simon, Rimassa, Lorenza, Di Tommaso, Luca, Vogel, Arndt, Michalak, Sophie, Boursier, Jérôme, Ziol, Marianne, Calderaro, Julien

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Abnormality-Driven Representation Learning for Radiology Imaging von Ligero, Marta, Lenz, Tim, Wölflein, Georg, Nahhas, Omar S. M. El, Truhn, Daniel, Kather, Jakob Nikolas

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Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology von Lenz, Tim, Nahhas, Omar S. M. El, Ligero, Marta, Kather, Jakob Nikolas

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Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization von Khader, Firas, Nahhas, Omar S. M. El, Han, Tianyu, Müller-Franzes, Gustav, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

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Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology von Nahhas, Omar S. M. El, Wölflein, Georg, Ligero, Marta, Lenz, Tim, van Treeck, Marko, Khader, Firas, Truhn, Daniel, Kather, Jakob Nikolas

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Benchmarking Pathology Feature Extractors for Whole Slide Image Classification von Wölflein, Georg, Ferber, Dyke, Meneghetti, Asier R, Nahhas, Omar S. M. El, Truhn, Daniel, Carrero, Zunamys I, Harrison, David J, Arandjelović, Ognjen, Kather, Jakob Nikolas

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Benchmarking foundation models as feature extractors for weakly-supervised computational pathology von 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

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Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology von Ferber, Dyke, Nahhas, Omar S. M. El, Wölflein, Georg, Wiest, Isabella C, Clusmann, Jan, Leßman, Marie-Elisabeth, Foersch, Sebastian, Lammert, Jacqueline, Tschochohei, Maximilian, Jäger, Dirk, Salto-Tellez, Manuel, Schultz, Nikolaus, Truhn, Daniel, Kather, Jakob Nikolas

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In-context learning enables multimodal large language models to classify cancer pathology images von Ferber, Dyke, Wölflein, Georg, Wiest, Isabella C, Ligero, Marta, Sainath, Srividhya, Laleh, Narmin Ghaffari, Nahhas, Omar S. M. El, Müller-Franzes, Gustav, Jäger, Dirk, Truhn, Daniel, Kather, Jakob Nikolas

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From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology von Nahhas, Omar S. M. El, van Treeck, Marko, Wölflein, Georg, Unger, Michaela, Ligero, Marta, Lenz, Tim, Wagner, Sophia J, Hewitt, Katherine J, Khader, Firas, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas

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Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology von Lenz, Tim, Omar S M El Nahhas, Ligero, Marta, Kather, Jakob Nikolas

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Regression-based Deep-Learning predicts molecular biomarkers from pathology slides von Nahhas, Omar S. M. El, Loeffler, Chiara M. L, Carrero, Zunamys I, van Treeck, Marko, Kolbinger, Fiona R, Hewitt, Katherine J, Muti, Hannah S, Graziani, Mara, Zeng, Qinghe, Calderaro, Julien, Ortiz-Brüchle, Nadina, Yuan, Tanwei, Hoffmeister, Michael, Brenner, Hermann, Brobeil, Alexander, Reis-Filho, Jorge S, Kather, Jakob Nikolas

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Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization von Khader, Firas, Omar S M El Nahhas, Han, Tianyu, Müller-Franzes, Gustav, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel

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