A Unifying Review of Deep and Shallow Anomaly Detection von Ruff, Lukas, Kauffmann, Jacob R., Vandermeulen, Robert A., Montavon, Gregoire, Samek, Wojciech, Kloft, Marius, Dietterich, Thomas G., Muller, Klaus-Robert

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Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma von Hägele, Miriam, Eschrich, Johannes, Ruff, Lukas, Alber, Maximilian, Schallenberg, Simon, Guillot, Adrien, Roderburg, Christoph, Tacke, Frank, Klauschen, Frederick

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1283 A novel, scalable deep learning-based approach to automated quality control of multiplex immunofluorescence images von Fink, Annika F, Schulte-Sasse, Roman, Bauw, Martin, Agrawal, Deepti, Perez, Beatriz, Sadeq, Mariam, Grote, Hans Juergen, Ohata, Yae, Ruff, Lukas, Alber, Maximilian, Ruane, Sharon, Mrowiec, Thomas

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1276 Leveraging artificial intelligence (AI) models delineating tumor vs immune cell expression for scalable biomarker analysis of clinical trial samples: a digital image analysis... von Böhm, Cornelius, Lingelbach, Katja, Pablos, Blanca, Heinke, Simon, Schallenberg, Simon, Krupar, Rosemarie, Aldoj, Nader, Pandhe, Suhas, Pizzagalli, Luca, Gottschalk, Maximilian, Wójtowicz, Bartosz, Tietz, Stephan, Rudolph, Marion, Ishii, Yuko, Ruff, Lukas, Ruane, Sharon, Alber, Maximilian, Schaer, David, Tomaso, Emmanuelle di, Pelekanou, Vasiliki

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970 Multiplex-immunofluorescence-based spatial characterization of the tumor-microenvironment of a large bicentric clinical non-small cell lung cancer cohort von Schallenberg, Simon, Dernbach, Gabriel, Ruane, Sharon, Böhm, Cornelius, Ruff, Lukas, Standvoss, Kai, Ghosh, Sandip, Dragomir, Mihnea P, Fritz, Rebecca, Koch, Ines, Friedrich, Corinna, Merkelbach-Bruse, Sabine, Quaas, Alexander, Frost, Nikolaj, Boschung, Kyrill, Randerath, Winfried, Schlachtenberger, Georg, Heldwein, Matthias, Keilholz, Ulrich, Hekmat, Khosro, Rückert, Jens-Carsten, Büttner, Reinhard, Vasaturo, Angela, Horst, David, Alber, Maximilian, Klauschen, Frederick

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Image Anomaly Detection with Generative Adversarial Networks von Deecke, Lucas, Vandermeulen, Robert, Ruff, Lukas, Mandt, Stephan, Kloft, Marius

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From Clustering to Cluster Explanations via Neural Networks von Kauffmann, Jacob, Esders, Malte, Ruff, Lukas, Montavon, Gregoire, Samek, Wojciech, Muller, Klaus-Robert

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Toward Explainable Artificial Intelligence for Precision Pathology von Klauschen, Frederick, Dippel, Jonas, Keyl, Philipp, Jurmeister, Philipp, Bockmayr, Michael, Mock, Andreas, Buchstab, Oliver, Alber, Maximilian, Ruff, Lukas, Montavon, Grégoire, Müller, Klaus-Robert

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Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study von Dernbach, Gabriel, Kazdal, Daniel, Ruff, Lukas, Alber, Maximilian, Romanovsky, Eva, Schallenberg, Simon, Christopoulos, Petros, Weis, Cleo-Aron, Muley, Thomas, Schneider, Marc A., Schirmacher, Peter, Thomas, Michael, Müller, Klaus-Robert, Budczies, Jan, Stenzinger, Albrecht, Klauschen, Frederick

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TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading von Schallenberg, Simon, Dernbach, Gabriel, Dragomir, Mihnea P, Schlachtenberger, Georg, Boschung, Kyrill, Friedrich, Corinna, Standvoss, Kai, Ruff, Lukas, Anders, Philipp, Grohé, Christian, Randerath, Winfried, Merkelbach-Bruse, Sabine, Quaas, Alexander, Heldwein, Matthias, Keilholz, Ulrich, Hekmat, Jens Khosro, Rückert, Carsten, Büttner, Reinhard, Horst, David, Klauschen, Frederick, Frost, Nikolaj

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Explainable artificial intelligence in pathology von Klauschen, Frederick, Dippel, Jonas, Keyl, Philipp, Jurmeister, Philipp, Bockmayr, Michael, Mock, Andreas, Buchstab, Oliver, Alber, Maximilian, Ruff, Lukas, Montavon, Grégoire, Müller, Klaus-Robert

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Abstract 5441: Cell cycle arrest status predicted from H&E stained images using deep learning von Aigner, Christina, Reichholf, Brian, Emschwiller, Maxime, Pezer, Marija, Winterhoff, Tobias, Schallenberg, Simon, Krupar, Rosemarie, Ruff, Lukas, Ruane, Sharon, Alber, Maximilian, Klauschen, Frederick, Trapani, Francesca

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Abstract 5423: AI powered quantification of mitotic rate in H&E stained tissue detects significant differences between treatment groups of preclinical pancreas cancer xenografts von Ruane, Sharon, Ruff, Lukas, Reichholf, Brian, Aigner, Christina, Barbuta, Emil, Tietz, Stephan, Atanaszov, Olivér, Krupar, Rosemarie, Schallenberg, Simon, Alber, Maximilian, Trapani, Francesca, Klauschen, Frederick

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Erklärbare Künstliche Intelligenz in der Pathologie von Klauschen, Frederick, Dippel, Jonas, Keyl, Philipp, Jurmeister, Philipp, Bockmayr, Michael, Mock, Andreas, Buchstab, Oliver, Alber, Maximilian, Ruff, Lukas, Montavon, Grégoire, Müller, Klaus-Robert

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Erklärbare Künstliche Intelligenz in der Pathologie von Klauschen, Frederick, Dippel, Jonas, Keyl, Philipp, Jurmeister, Philipp, Bockmayr, Michael, Mock, Andreas, Buchstab, Oliver, Alber, Maximilian, Ruff, Lukas, Montavon, Grégoire, Müller, Klaus-Robert

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The Clever Hans Effect in Unsupervised Learning von Kauffmann, Jacob, Dippel, Jonas, Ruff, Lukas, Samek, Wojciech, Müller, Klaus-Robert, Montavon, Grégoire

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Geometric Disentanglement by Random Convex Polytopes von Joswig, Michael, Kaluba, Marek, Ruff, Lukas

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xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer von Sextro, Marvin, Dernbach, Gabriel, Standvoss, Kai, Schallenberg, Simon, Klauschen, Frederick, Müller, Klaus-Robert, Alber, Maximilian, Ruff, Lukas

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Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification von Chong, Penny, Ruff, Lukas, Kloft, Marius, Binder, Alexander

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AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics von Dippel, Jonas, Prenißl, Niklas, Hense, Julius, Liznerski, Philipp, Winterhoff, Tobias, Schallenberg, Simon, Kloft, Marius, Buchstab, Oliver, Horst, David, Alber, Maximilian, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick

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