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Titus Josef Brinker

German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group

SCHOLARLY PAPERS

3

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393

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Scholarly Papers (3)

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1.

A 3-Dimensional Histology Computer Model of Malignant Melanoma and its Implications for Digital Pathology

Number of pages: 13 Posted: 09 Aug 2023
Alexander Kurz, Dieter Krahl, Heinz Kutzner, Raymond Barnhill, Antonio Perasole, Maria Teresa Fernandez Figueras, Gerardo Ferrara, Stephan A. Braun, Hans Starz, Mar Llamas-Velasco, Jochen Utikal, Stefan Fröhling, Christof von Kalle, Jakob Nikolas Kather, Lucas Schneider and Titus Josef Brinker
German Cancer Research Center (DKFZ), Dres. Krahl Dermatopathology, Dermatopathology Friedrichshafen, Université Paris-Dauphine, PSL Research University - Institut Curie, Cerba Healthcare S.r.l. Rete Diagnostica Italiana, International University of Catalonia, Fondazione IRCCS Istituto Nazionale dei Tumori, University Hospital Münster, Dermpath München, Hospital Universitario La Princesa, Heidelberg University - Department of Dermatology, German Cancer Research Center (DKFZ), Berlin Institute of Health, Technical University of Dresden, German Cancer Research Center (DKFZ) and German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group
Downloads 250 (306,221)

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Dermatology, digital pathology, Dermatopathology, melanoma, Artificial Intelligence, deep learning

2.

Ai-Generated Cancer Prevention Influencers Can Target Risk Groups on Social Media at Low Cost

Number of pages: 17 Posted: 19 Dec 2024
Titus Josef Brinker, Jana Therés Winterstein, Julia Abels, Arlene Kuehn, Nicolas Carl and Christoph Wies
German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and German Cancer Research Center (DKFZ)
Downloads 101 (689,572)

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Targeted Prevention, GenAI, AI-generated Influencers, Digital Health Campaigns, Health Communication, Risk Groups, Cancer Prevention, Social Media, prevention

3.

Deep Learning to Predict Breast Cancer Sentinel Lymph Node Status on Insema Histological Images

Number of pages: 15 Posted: 26 Sep 2023
Frederik Marmé, Eva Isabelle Krieghoff-Henning, Bernd Gerber, Max Schmitt, Dirk-Michael Zahm, Dirk Olaf Bauerschlag, Helmut Forstbauer, Guido Hildebrandt, Beyhan Ataseven, Tobias Brodkorb, Carsten Denkert, Angrit Stachs, David Krug, Joerg Heil, Michael Golatta, Thorsten Kuehn, Valentina Nekljudova, Timo Gaiser, Rebecca Schönmehl, Christoph Brochhausen, Sibylle Loibl, Toralf Reimer and Titus Josef Brinker
University of Heidelberg - Department of Obstetrics and Gynaecology, Medical Faculty Mannheim, German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group, University Hospital of Rostock - Department of Obstetrics and Gynecology, German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group, SRH Waldklinikum Gera GmbH, Universitätsfrauenklinik Aachen, GOSPL - Gesellschaft für onkologische Studien, University Medicine Rostock - Department of Radiotherapy, Hebrew University of Jerusalem, University of Heidelberg - Department of Obstetrics and Gynaecology, Medical Faculty Mannheim, University of Marburg - Institute for Pathology, University Hospital of Rostock - Department of Obstetrics and Gynecology, Universitätsklinikum Schleswig-Holstein - Klinik für Strahlentherapie, Klinik St. Elisabeth - Brustzentrum Heidelberg, Klinik St. Elisabeth - Brustzentrum Heidelberg, Klinikum Esslingen, German Breast Group (GBG Forschungs GmbH), University of Heidelberg - Institute of Pathology, Medical Faculty Mannheim, University of Heidelberg - Institute of Pathology, Medical Faculty Mannheim, University of Heidelberg - Institute of Pathology, Medical Faculty Mannheim, German Breast Group (GBG Forschungs GmbH), University Hospital of Rostock - Department of Obstetrics and Gynecology and German Cancer Research Center (DKFZ) - Digital Biomarkers for Oncology Group
Downloads 42 (1,170,409)

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Sentinel, lymph node status, deep learning, breast cancer, digital biomarker