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Sebastian Foersch

Showing results (51-60 of 73) with videos related to

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The Journal of Pathology. Clinical Research|August 23, 2023
High expression of insulinoma-associated protein 1 (INSM1) distinguishes colorectal mixed and pure neuroendocrine carcinomas from conventional adenocarcinomas with diffuse expression of synaptophysinAnne-Sophie Litmeyer, Björn Konukiewitz, Atsuko Kasajima, et al.
The American Journal of Surgical Pathology|June 9, 2021
Morphology Matters: A Critical Reappraisal of the Clinical Relevance of Morphologic Criteria From the 2019 WHO Classification in a Large Colorectal Cancer Cohort Comprising 1004 CasesMoritz Jesinghaus, Maxime Schmitt, Corinna Lang, et al.
Cancers|October 23, 2021
Neuroendocrine Differentiation in Conventional Colorectal Adenocarcinomas: Incidental Finding or Prognostic Biomarker?Björn Konukiewitz, Atsuko Kasajima, Maxime Schmitt, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Endocrine Pathology|March 28, 2025
DLL3 Expression in Neuroendocrine Carcinomas and Neuroendocrine Tumours: Insights From a Multicentric Cohort of 1294 Pulmonary and Extrapulmonary Neuroendocrine NeoplasmsMaxime Schmitt, Hanibal Bohnenberger, Detlef Klaus Bartsch, et al.
The Lancet. Digital Health|December 20, 2023
End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre studyXiaofeng Jiang, Michael Hoffmeister, Hermann Brenner, et al.
Cancer Research|June 17, 2026
Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in PathologyLaura Žigutytė, Tim Lenz, Tianyu Han, et al.
The Journal of Pathology. Clinical Research|March 20, 2024
A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testingMiriam Angeloni, Thomas van Doeveren, Sebastian Lindner, et al.
Thyroid : Official Journal of the American Thyroid Association|July 3, 2025
Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid CarcinomaIngrid Marion, Stefan Schulz, Christina Glasner, et al.
Journal of Cancer Research and Clinical Oncology|March 15, 2023
An overview and a roadmap for artificial intelligence in hematology and oncologyWiebke Rösler, Michael Altenbuchinger, Bettina Baeßler, et al.
Pageof 8

Showing results (51-60 of 73) with videos related to

Sort By:
Pageof 8
The Journal of Pathology. Clinical Research|August 23, 2023
High expression of insulinoma-associated protein 1 (INSM1) distinguishes colorectal mixed and pure neuroendocrine carcinomas from conventional adenocarcinomas with diffuse expression of synaptophysinAnne-Sophie Litmeyer, Björn Konukiewitz, Atsuko Kasajima, et al.
The American Journal of Surgical Pathology|June 9, 2021
Morphology Matters: A Critical Reappraisal of the Clinical Relevance of Morphologic Criteria From the 2019 WHO Classification in a Large Colorectal Cancer Cohort Comprising 1004 CasesMoritz Jesinghaus, Maxime Schmitt, Corinna Lang, et al.
Cancers|October 23, 2021
Neuroendocrine Differentiation in Conventional Colorectal Adenocarcinomas: Incidental Finding or Prognostic Biomarker?Björn Konukiewitz, Atsuko Kasajima, Maxime Schmitt, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Endocrine Pathology|March 28, 2025
DLL3 Expression in Neuroendocrine Carcinomas and Neuroendocrine Tumours: Insights From a Multicentric Cohort of 1294 Pulmonary and Extrapulmonary Neuroendocrine NeoplasmsMaxime Schmitt, Hanibal Bohnenberger, Detlef Klaus Bartsch, et al.
The Lancet. Digital Health|December 20, 2023
End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre studyXiaofeng Jiang, Michael Hoffmeister, Hermann Brenner, et al.
Cancer Research|June 17, 2026
Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in PathologyLaura Žigutytė, Tim Lenz, Tianyu Han, et al.
The Journal of Pathology. Clinical Research|March 20, 2024
A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testingMiriam Angeloni, Thomas van Doeveren, Sebastian Lindner, et al.
Thyroid : Official Journal of the American Thyroid Association|July 3, 2025
Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid CarcinomaIngrid Marion, Stefan Schulz, Christina Glasner, et al.
Journal of Cancer Research and Clinical Oncology|March 15, 2023
An overview and a roadmap for artificial intelligence in hematology and oncologyWiebke Rösler, Michael Altenbuchinger, Bettina Baeßler, et al.
Pageof 8