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948 results on '"Klauschen, Frederick"'

1. xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer

Author

Sextro, Marvin, Dernbach, Gabriel, Standvoss, Kai, Schallenberg, Simon, Klauschen, Frederick, Müller, Klaus-Robert, Alber, Maximilian, and Ruff, Lukas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent advances in the spatial modeling of the tumor microenvironment using graph neural networks, we present an explainable cell graph (xCG) approach for survival prediction. We validate our model on a public cohort of imaging mass cytometry (IMC) data for 416 cases of lung adenocarcinoma. We explain survival predictions in terms of known phenotypes on the cell level by computing risk attributions over cell graphs, for which we propose an efficient grid-based layer-wise relevance propagation (LRP) method. Our ablation studies highlight the importance of incorporating the cancer stage and model ensembling to improve the quality of risk estimates. Our xCG method, together with the IMC data, is made publicly available to support further research., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 11 pages

Published
2024

2. AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics

Author

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, and Klauschen, Frederick

Subjects
Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for common diseases. In clinical reality, however, only few diseases are common, whereas the majority of diseases are less frequent (long-tail distribution). Current AI models overlook or misclassify these diseases. We propose a deep anomaly detection approach that only requires training data from common diseases to detect also all less frequent diseases. We collected two large real-world datasets of gastrointestinal biopsies, which are prototypical of the problem. Herein, the ten most common findings account for approximately 90% of cases, whereas the remaining 10% contained 56 disease entities, including many cancers. 17 million histological images from 5,423 cases were used for training and evaluation. Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95.0% (stomach) and 91.0% (colon) AUROC and generalized across scanners and hospitals. By design, the proposed anomaly detection can be expected to detect any pathological alteration in the diagnostic tail of gastrointestinal biopsies, including rare primary or metastatic cancers. This study establishes the first effective clinical application of AI-based anomaly detection in histopathology that can flag anomalous cases, facilitate case prioritization, reduce missed diagnoses and enhance the general safety of AI models, thereby driving AI adoption and automation in routine diagnostics and beyond.

Published
2024

3. xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

Author

Hense, Julius, Idaji, Mina Jamshidi, Eberle, Oliver, Schnake, Thomas, Dippel, Jonas, Ciernik, Laure, Buchstab, Oliver, Mock, Andreas, Klauschen, Frederick, and Müller, Klaus-Robert

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication. However, MIL explanation methods are still lagging behind, as they are limited to small bag sizes or disregard instance interactions. We revisit MIL through the lens of explainable AI (XAI) and introduce xMIL, a refined framework with more general assumptions. We demonstrate how to obtain improved MIL explanations using layer-wise relevance propagation (LRP) and conduct extensive evaluation experiments on three toy settings and four real-world histopathology datasets. Our approach consistently outperforms previous explanation attempts with particularly improved faithfulness scores on challenging biomarker prediction tasks. Finally, we showcase how xMIL explanations enable pathologists to extract insights from MIL models, representing a significant advance for knowledge discovery and model debugging in digital histopathology. Codes are available at: https://github.com/tubml-pathology/xMIL.

Published
2024

4. Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Author

Schmitt, Max, Maron, Roman Christoph, Hekler, Achim, Stenzinger, Albrecht, Hauschild, Axel, Weichenthal, Michael, Tiemann, Markus, Krahl, Dieter, Kutzner, Heinz, Utikal, Jochen Sven, Haferkamp, Sebastian, Kather, Jakob Nikolas, Klauschen, Frederick, Krieghoff-Henning, Eva, Fröhling, Stefan, von Kalle, Christof, and Brinker, Titus Josef

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAn increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. ObjectiveThe objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. MethodsWe trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. ResultsA mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). ConclusionsBecause all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Published
2021
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6. Proteomic profiling reveals ACSS2 facilitating metabolic support in acute myeloid leukemia

Author

Mochmann, Liliana H., Treue, Denise, Bockmayr, Michael, Silva, Patricia, Zasada, Christin, Mastrobuoni, Guido, Bayram, Safak, Forbes, Martin, Jurmeister, Philipp, Liebig, Sven, Blau, Olga, Schleich, Konstanze, Splettstoesser, Bianca, Nordmann, Thierry M., von der Heide, Eva K., Isaakidis, Konstandina, Schulze, Veronika, Busch, Caroline, Siddiq, Hafsa, Schlee, Cornelia, Hester, Svenja, Fransecky, Lars, Neumann, Martin, Kempa, Stefan, Klauschen, Frederick, and Baldus, Claudia D.

Published
2024
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8. RudolfV: A Foundation Model by Pathologists for Pathologists

Author

Dippel, Jonas, Feulner, Barbara, Winterhoff, Tobias, Milbich, Timo, Tietz, Stephan, Schallenberg, Simon, Dernbach, Gabriel, Kunft, Andreas, Heinke, Simon, Eich, Marie-Lisa, Ribbat-Idel, Julika, Krupar, Rosemarie, Anders, Philipp, Prenißl, Niklas, Jurmeister, Philipp, Horst, David, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick, and Alber, Maximilian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to generalization, application variety, and handling rare diseases. Recent efforts introduced self-supervised foundation models to address these challenges, yet existing approaches do not leverage pathologist knowledge by design. In this study, we present a novel approach to designing foundation models for computational pathology, incorporating pathologist expertise, semi-automated data curation, and a diverse dataset from over 15 laboratories, including 58 tissue types, and encompassing 129 different histochemical and immunohistochemical staining modalities. We demonstrate that our model "RudolfV" surpasses existing state-of-the-art foundation models across different benchmarks focused on tumor microenvironment profiling, biomarker evaluation, and reference case search while exhibiting favorable robustness properties. Our study shows how domain-specific knowledge can increase the efficiency and performance of pathology foundation models and enable novel application areas.

Published
2024

10. DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology

Author

Aversa, Marco, Nobis, Gabriel, Hägele, Miriam, Standvoss, Kai, Chirica, Mihaela, Murray-Smith, Roderick, Alaa, Ahmed, Ruff, Lukas, Ivanova, Daniela, Samek, Wojciech, Klauschen, Frederick, Sanguinetti, Bruno, and Oala, Luis

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our approach first generates synthetic segmentation masks, subsequently used as conditions for the high-fidelity generative diffusion process. The proposed sampling method can be scaled up to any desired image size while only requiring small patches for fast training. Moreover, it can be parallelized more efficiently than previous large-content generation methods while avoiding tiling artifacts. The training leverages classifier-free guidance to augment a small, sparsely annotated dataset with unlabelled data. Our method alleviates unique challenges in histopathological imaging practice: large-scale information, costly manual annotation, and protective data handling. The biological plausibility of DiffInfinite data is evaluated in a survey by ten experienced pathologists as well as a downstream classification and segmentation task. Samples from the model score strongly on anti-copying metrics which is relevant for the protection of patient data.

Published
2023

14. Metabolic pathway-based subtypes associate glycan biosynthesis and treatment response in head and neck cancer

Author

Dankó, Benedek, Hess, Julia, Unger, Kristian, Samaga, Daniel, Walz, Christoph, Walch, Axel, Sun, Na, Baumeister, Philipp, Zeng, Peter Y. F., Walter, Franziska, Marschner, Sebastian, Späth, Richard, Gires, Olivier, Herkommer, Timm, Dazeh, Ramin, Matos, Thaina, Kreutzer, Lisa, Matschke, Johann, Eul, Katharina, Klauschen, Frederick, Pflugradt, Ulrike, Canis, Martin, Ganswindt, Ute, Mymryk, Joe S., Wollenberg, Barbara, Nichols, Anthony C., Belka, Claus, Zitzelsberger, Horst, Lauber, Kirsten, and Selmansberger, Martin

Published
2024
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16. Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides

Author

Hägele, Miriam, Eschrich, Johannes, Ruff, Lukas, Alber, Maximilian, Schallenberg, Simon, Guillot, Adrien, Roderburg, Christoph, Tacke, Frank, and Klauschen, Frederick

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong to. To integrate these labels, we formulate a complementary loss for segmentation. Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model. On the task of diagnostic differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma, we achieve a balanced accuracy of 0.91 (CI 95%: 0.86 - 0.95) at case level for 165 hold-out patients. Furthermore, we also show that leveraging complementary labels improves the robustness of segmentation and increases performance at case level.

Published
2023

17. Benchmarking whole exome sequencing in the German network for personalized medicine

Author

Menzel, Michael, Martis-Thiele, Mihaela, Goldschmid, Hannah, Ott, Alexander, Romanovsky, Eva, Siemanowski-Hrach, Janna, Seillier, Lancelot, Brüchle, Nadina Ortiz, Maurer, Angela, Lehmann, Kjong-Van, Begemann, Matthias, Elbracht, Miriam, Meyer, Robert, Dintner, Sebastian, Claus, Rainer, Meier-Kolthoff, Jan P., Blanc, Eric, Möbs, Markus, Joosten, Maria, Benary, Manuela, Basitta, Patrick, Hölscher, Florian, Tischler, Verena, Groß, Thomas, Kutz, Oliver, Prause, Rebecca, William, Doreen, Horny, Kai, Goering, Wolfgang, Sivalingam, Sugirthan, Borkhardt, Arndt, Blank, Cornelia, Junk, Stefanie V., Yasin, Layal, Moskalev, Evgeny A., Carta, Maria Giulia, Ferrazzi, Fulvia, Tögel, Lars, Wolter, Steffen, Adam, Eugen, Matysiak, Uta, Rosenthal, Tessa, Dönitz, Jürgen, Lehmann, Ulrich, Schmidt, Gunnar, Bartels, Stephan, Hofmann, Winfried, Hirsch, Steffen, Dikow, Nicola, Göbel, Kirsten, Banan, Rouzbeh, Hamelmann, Stefan, Fink, Annette, Ball, Markus, Neumann, Olaf, Rehker, Jan, Kloth, Michael, Murtagh, Justin, Hartmann, Nils, Jurmeister, Phillip, Mock, Andreas, Kumbrink, Jörg, Jung, Andreas, Mayr, Eva-Maria, Jacob, Anne, Trautmann, Marcel, Kirmse, Santina, Falkenberg, Kim, Ruckert, Christian, Hirsch, Daniela, Immel, Alexander, Dietmaier, Wolfgang, Haack, Tobias, Marienfeld, Ralf, Fürstberger, Axel, Niewöhner, Jakob, Gerstenmaier, Uwe, Eberhardt, Timo, Greif, Philipp A., Appenzeller, Silke, Maurus, Katja, Doll, Julia, Jelting, Yvonne, Jonigk, Danny, Märkl, Bruno, Beule, Dieter, Horst, David, Wulf, Anna-Lena, Aust, Daniela, Werner, Martin, Reuter-Jessen, Kirsten, Ströbel, Philipp, Auber, Bernd, Sahm, Felix, Merkelbach-Bruse, Sabine, Siebolts, Udo, Roth, Wilfried, Lassmann, Silke, Klauschen, Frederick, Gaisa, Nadine T., Weichert, Wilko, Evert, Matthias, Armeanu-Ebinger, Sorin, Ossowski, Stephan, Schroeder, Christopher, Schaaf, Christian P., Malek, Nisar, Schirmacher, Peter, Kazdal, Daniel, Pfarr, Nicole, Budczies, Jan, and Stenzinger, Albrecht

Published
2024
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19. Testing for deficient mismatch repair and microsatellite instability: A focused update

Author

Rüschoff, Josef, Schildhaus, Hans-Ulrich, Rüschoff, Jan Hendrik, Jöhrens, Korinna, Bocker Edmonston, Tina, Dietmaier, Wolfgang, Bläker, Hendrik, Baretton, Gustavo, Horst, David, Dietel, Manfred, Hartmann, Arndt, Klauschen, Frederick, Merkelbach-Bruse, Sabine, Stenzinger, Albrecht, Schöniger, Sandra, Tiemann, Markus, Weichert, Wilko, and Büttner, Reinhard

Published
2023
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22. A Retrospective Analysis of Biliary Tract Cancer Patients Presented to the Molecular Tumor Board at the Comprehensive Cancer Center Munich

Author

Zhang, Danmei, Dorman, Klara, Heinrich, Kathrin, Weiss, Lena, Boukovala, Myrto, Haas, Michael, Greif, Philipp A., Ziemann, Frank, Beyer, Georg, Roessler, Daniel, Goni, Elisabetta, Renz, Bernhard, D’Haese, Jan G., Kunz, Wolfgang G., Seidensticker, Max, Corradini, Stefanie, Niyazi, Maximilian, Ormanns, Steffen, Kumbrink, Jörg, Jung, Andreas, Mock, Andreas, Rudelius, Martina, Klauschen, Frederick, Werner, Jens, Mayerle, Julia, von Bergwelt-Baildon, Michael, Boeck, Stefan, Heinemann, Volker, and Westphalen, C. Benedikt

Published
2023
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23. Testung auf Mismatch-Reparatur-Defizienz und Mikrosatelliteninstabilität: Eine fokussierte Aktualisierung

Author

Rüschoff, Josef, Schildhaus, Hans-Ulrich, Rüschoff, Jan Hendrik, Jöhrens, Korinna, Bocker-Edmonston, Tina, Dietmaier, Wolfgang, Bläker, Hendrik, Baretton, Gustavo, Horst, David, Dietel, Manfred, Hartmann, Arndt, Klauschen, Frederick, Merkelbach-Bruse, Sabine, Stenzinger, Albrecht, Schöniger, Sandra, Tiemann, Markus, Weichert, Wilko, and Büttner, Reinhard

Published
2023
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24. DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification

Author

Jurmeister, Philipp, Leitheiser, Maximilian, Arnold, Alexander, Capilla, Emma Payá, Mochmann, Liliana H., Zhdanovic, Yauheniya, Schleich, Konstanze, Jung, Nina, Chimal, Edgar Calderon, Jung, Andreas, Kumbrink, Jörg, Harter, Patrick, Prenißl, Niklas, Elezkurtaj, Sefer, Brcic, Luka, Deigendesch, Nikolaus, Frank, Stephan, Hench, Jürgen, Försch, Sebastian, Breimer, Gerben, van Engen van Grunsven, Ilse, Lassche, Gerben, van Herpen, Carla, Zhou, Fang, Snuderl, Matija, Agaimy, Abbas, Müller, Klaus-Robert, von Deimling, Andreas, Capper, David, Klauschen, Frederick, and Ihrler, Stephan

Published
2024
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26. Explaining Bayesian Neural Networks

Author

Bykov, Kirill, Höhne, Marina M. -C., Creosteanu, Adelaida, Müller, Klaus-Robert, Klauschen, Frederick, Nakajima, Shinichi, and Kloft, Marius

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

To make advanced learning machines such as Deep Neural Networks (DNNs) more transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs' predictions. These interpretations are usually given in the form of heatmaps, each one illustrating relevant patterns regarding the prediction for a given instance. Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances. In this work, we bring together these two perspectives of transparency into a holistic explanation framework for explaining BNNs. Within the Bayesian framework, the network weights follow a probability distribution. Hence, the standard (deterministic) prediction strategy of DNNs extends in BNNs to a predictive distribution, and thus the standard explanation extends to an explanation distribution. Exploiting this view, we uncover that BNNs implicitly employ multiple heterogeneous prediction strategies. While some of these are inherited from standard DNNs, others are revealed to us by considering the inherent uncertainty in BNNs. Our quantitative and qualitative experiments on toy/benchmark data and real-world data from pathology show that the proposed approach of explaining BNNs can lead to more effective and insightful explanations., Comment: 16 pages, 8 figures

Published
2021

27. Slow integrin-dependent migration organizes networks of tissue-resident mast cells

Author

Kaltenbach, Lukas, Martzloff, Paloma, Bambach, Sarah K., Aizarani, Nadim, Mihlan, Michael, Gavrilov, Alina, Glaser, Katharina M., Stecher, Manuel, Thünauer, Roland, Thiriot, Aude, Heger, Klaus, Kierdorf, Katrin, Wienert, Stephan, von Andrian, Ulrich H., Schmidt-Supprian, Marc, Nerlov, Claus, Klauschen, Frederick, Roers, Axel, Bajénoff, Marc, Grün, Dominic, and Lämmermann, Tim

Published
2023
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29. Outcome of First-Line Treatment With Pembrolizumab According to KRAS/TP53 Mutational Status for Nonsquamous Programmed Death-Ligand 1–High (≥50%) NSCLC in the German National Network Genomic Medicine Lung Cancer

Author

Hilbrandt, Moritz, Süptitz, Juliane, Grah, Christian, Velthaus, Janna-Lisa, Kopp, Hans-Georg, Schmidt, Bernd, Horter, Susanne, Keymel, Stefanie, Aydilek, Enver, Tritchkova, Guergana, Raspe, Matthias, Papić, Dražen, Florian, Stefan, Horst, David, Wild, Peter J., Thomas, Michael, Grohé, Christian, Bleckmann, Annalen, Wermke, Martin, Hummel, Horst-Dieter, Stratmann, Jan, Schütte, Wolfgang, Bischoff, Philip, Reck, Martin, Overbeck, Tobias, Christopoulos, Petros, Rittmeyer, Achim, Lüders, Heike, Kollmeier, Jens, Kulhavy, Jonas, Kemper, Marcel, Reinmuth, Niels, Röper, Julia, Janning, Melanie, Sommer, Linna, Aguinarte, Lukas, Koch, Myriam, Wiesweg, Marcel, Wesseler, Claas, Waller, Cornelius F., Kauffmann-Guerrero, Diego, Stenzinger, Albrecht, Stephan-Falkenau, Susann, Trautmann, Marcel, Lassmann, Silke, Tiemann, Markus, Klauschen, Frederick, Sebastian, Martin, Griesinger, Frank, Wolf, Jürgen, Loges, Sonja, and Frost, Nikolaj

Published
2024
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33. Evaluation of the effectiveness of a nationwide precision medicine program for patients with advanced non-small cell lung cancer in Germany: a historical cohort analysis

Author

Kästner, Anika, Kron, Anna, van den Berg, Neeltje, Moon, Kilson, Scheffler, Matthias, Schillinger, Gerhard, Pelusi, Natalie, Hartmann, Nils, Rieke, Damian Tobias, Stephan-Falkenau, Susann, Schuler, Martin, Wermke, Martin, Weichert, Wilko, Klauschen, Frederick, Haller, Florian, Hummel, Horst-Dieter, Sebastian, Martin, Gattenlöhner, Stefan, Bokemeyer, Carsten, Esposito, Irene, Jakobs, Florian, von Kalle, Christof, Büttner, Reinhard, Wolf, Jürgen, and Hoffmann, Wolfgang

Published
2024
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34. TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading

Author

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, and Frost, Nikolaj

Published
2024
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36. Lessons learned: the first consecutive 1000 patients of the CCCMunichLMU Molecular Tumor Board

Author

Heinrich, Kathrin, Miller-Phillips, Lisa, Ziemann, Frank, Hasselmann, Korbinian, Rühlmann, Katharina, Flach, Madeleine, Biro, Dorottya, von Bergwelt-Baildon, Michael, Holch, Julian, Herold, Tobias, von Baumgarten, Louisa, Greif, Philipp A., Jeremias, Irmela, Wuerstlein, Rachel, Casuscelli, Jozefina, Spitzweg, Christine, Seidensticker, Max, Renz, Bernhard, Corradini, Stefanie, Baumeister, Philipp, Goni, Elisabetta, Tufman, Amanda, Jung, Andreas, Kumbrink, Jörg, Kirchner, Thomas, Klauschen, Frederick, Metzeler, Klaus H., Heinemann, Volker, and Westphalen, C. Benedikt

Published
2023
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37. Precision Oncology in Pancreatic Cancer: Experiences and Challenges of the CCCMunichLMU Molecular Tumor Board

Author

Dorman, Klara, Zhang, Danmei, Heinrich, Kathrin, Reeh, Laurens, Weiss, Lena, Haas, Michael, Beyer, Georg, Rössler, Daniel, Goni, Elisabetta, Renz, Bernhard W., D’Haese, Jan G., Kunz, Wolfgang G., Seidensticker, Max, Corradini, Stefanie, Niyazi, Maximilian, Ormanns, Steffen, Kumbrink, Jörg, Jung, Andreas, Klauschen, Frederick, Werner, Jens, Mayerle, Julia, von Bergwelt-Baildon, Michael, Boeck, Stefan, Heinemann, Volker, and Westphalen, C. Benedikt

Published
2023
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43. Erratum zu: Testung auf Mismatch-Reparatur-Defizienz und Mikrosatelliteninstabilität

Author

Rüschoff, Josef, Schildhaus, Hans-Ulrich, Rüschoff, Jan Hendrik, Jöhrens, Korinna, Bocker-Edmonston, Tina, Dietmaier, Wolfgang, Bläker, Hendrik, Baretton, Gustavo, Horst, David, Dietel, Manfred, Hartmann, Arndt, Klauschen, Frederick, Merkelbach-Bruse, Sabine, Stenzinger, Albrecht, Schöniger, Sandra, Tiemann, Markus, Weichert, Wilko, and Büttner, Reinhard

Published
2023
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44. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

Author

Hägele, Miriam, Seegerer, Philipp, Lapuschkin, Sebastian, Bockmayr, Michael, Samek, Wojciech, Klauschen, Frederick, Müller, Klaus-Robert, and Binder, Alexander

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, explanation methods have emerged, which are so far still rarely used in medicine. This work shows their application to generate heatmaps that allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. These challenges comprise biases typically inherent to histopathology data. We study binary classification tasks of tumor tissue discrimination in publicly available haematoxylin and eosin slides of various tumor entities and investigate three types of biases: (1) biases which affect the entire dataset, (2) biases which are by chance correlated with class labels and (3) sampling biases. While standard analyses focus on patch-level evaluation, we advocate pixel-wise heatmaps, which offer a more precise and versatile diagnostic instrument and furthermore help to reveal biases in the data. This insight is shown to not only detect but also to be helpful to remove the effects of common hidden biases, which improves generalization within and across datasets. For example, we could see a trend of improved area under the receiver operating characteristic curve by 5% when reducing a labeling bias. Explanation techniques are thus demonstrated to be a helpful and highly relevant tool for the development and the deployment phases within the life cycle of real-world applications in digital pathology.

Published
2019
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46. Struktur und Inhalt der EU-IVDR: Bestandsaufnahme und Implikationen für die Pathologie

Author

Kahles, Andy, Goldschmid, Hannah, Volckmar, Anna-Lena, Plöger, Carolin, Kazdal, Daniel, Penzel, Roland, Budczies, Jan, Kempny, Gisela, Kazmierczak, Marlon, Flechtenmacher, Christa, Baretton, Gustavo, Weichert, Wilko, Horst, David, Klauschen, Frederick, Gassner, Ulrich M., Brüggemann, Monika, Vogeser, Michael, Schirmacher, Peter, and Stenzinger, Albrecht

Published
2022
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47. Imaging bridges pathology and radiology

Author

Hansmann, Martin-Leo, Klauschen, Frederick, Samek, Wojciech, Müller, Klaus-Robert, Donnadieu, Emmanuel, Scharf, Sonja, Hartmann, Sylvia, Koch, Ina, Ackermann, Jörg, Pantanowitz, Liron, Schäfer, Hendrik, and Wurzel, Patrick

Published
2023
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48. Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Author

Binder, Alexander, Bockmayr, Michael, Hägele, Miriam, Wienert, Stephan, Heim, Daniel, Hellweg, Katharina, Stenzinger, Albrecht, Parlow, Laura, Budczies, Jan, Goeppert, Benjamin, Treue, Denise, Kotani, Manato, Ishii, Masaru, Dietel, Manfred, Hocke, Andreas, Denkert, Carsten, Müller, Klaus-Robert, and Klauschen, Frederick

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment.

Published
2018

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