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Journal Articles

Synthetic Bone Marrow Smears Are a Privacy-Preserving Substitute for Developing Accurate Leukemia Classification Models in Hematological Microscopy

Jan-Niklas Eckardt, Ishan Srivastava, Zizhe Wang, Susann Winter, Tim Schmittmann, Sebastian Riechert, Miriam Eva Helena Gediga, Anas Shekh Sulaiman, Martin M. K. Schneider, Freya Schulze, Christian Thiede, Katja Sockel, Frank P. Kroschinsky, Christoph Röllig, Martin Bornhäuser, Karsten Wendt, Jan Moritz Middeke

Blood (2024) 144 (Supplement 1): 1057.

https://doi.org/10.1182/blood-2024-198486

Published: 2024

Journal Articles

Autonomous Region-of-Interest Detection, Cell Segmentation and Cell Classification in Bone Marrow Cytomorphology Using Deep Learning

Dante Adami, Sebastian Riechert, Tim Schmittmann, Markus Badstübner, Karsten Wendt, Stefani Barbara Parmentier, Katja Sockel, Frank P. Kroschinsky, Martin Bornhäuser, Jan Moritz Middeke, Jan-Niklas Eckardt

Blood (2024) 144 (Supplement 1): 4975.

https://doi.org/10.1182/blood-2024-205235

Published: 2024

Journal Articles

Age-Stratified Game-Theory-Informed Machine Learning of Molecular Alterations Unveils Prognostic Divergence in 3062 Pediatric and Adult Acute Myeloid Leukemia Patients

Blood (2024) 144 (Supplement 1): 2211.

https://doi.org/10.1182/blood-2024-198861

Published: 2024

Journal Articles

The Differential Impact of Secondary-Type Mutations in a Multinational Cohort of 5311 Intensively Treated Acute Myeloid Leukemia Patients

Jan-Niklas Eckardt, Marius Bill, Konstanze Döhner, Christian Rausch, Klaus H Metzeler, Karsten Spiekermann, Sebastian Stasik, Tim Sauer, Sebastian Scholl, Andreas Hochhaus, Martina Crysandt, Tim H. H. Brummendorf, Utz Krug, Bernhard Wörmann, Hermann Einsele, Wolfgang Hiddemann, Dennis Görlich, Maria Cristina Sauerland, Björn Steffen, Andreas Neubauer, Andreas Burchert, Kerstin Schaefer-Eckart, Wolfgang E. Berdel, Christoph Schliemann, Stefan W. Krause, Mathias Hänel, Maher Hanoun, Martin Kaufmann, Lars Fransecky, Jan Braess, Johannes Schetelig, Jan Moritz Middeke, Lars Bullinger, Michael Heuser, Felicitas R Thol, Hubert Serve, Claudia D Baldus, Uwe Platzbecker, Carsten Mueller-Tidow, Barbora Weinbergerova, Jiri Mayer, Pierre-Yves Dumas, Sarah Bertoli, Eric Delabesse, Christian Récher, Arnaud Pigneux, Tobias Herold, Arnold Ganser, Hartmut Döhner, Martin Bornhäuser, Christian Thiede, Christoph Röllig

Blood (2024) 144 (Supplement 1): 2934.

https://doi.org/10.1182/blood-2024-201945

Published: 2024

Journal Articles

Deep Learning Predicts Response to Venetoclax and Azacitidine in Patients with Newly Diagnosed Acute Myeloid Leukemia Solely Based on Bone Marrow Smear Image Data

Jan-Niklas Eckardt, Ishan Srivastava, Yeqiao Xu, Kellen B Gil, Andrea A. Osypuk, Zenggang Pan, Martin M. K. Schneider, Freya Schulze, Karsten Wendt, Susann Winter, Martin Bornhäuser, Daniel A. Pollyea, Jan Moritz Middeke

Blood (2024) 144 (Supplement 1): 2917.

https://doi.org/10.1182/blood-2024-206776

Published: 2024

Journal Articles

Supervised Machine Learning Predicts Transformation Risk and Key Features throughout the Patient Journey in Myelodysplastic Neoplasms

Freya Schulze, Waldemar Hahn, Katja Sockel, Susann Winter, Markus Wolfien, Martin Bornhaeuser, Jan Moritz Middeke, Jan-Niklas Eckardt

Blood (2024) 144 (Supplement 1): 1822.

https://doi.org/10.1182/blood-2024-210870

Published: 2024

Journal Articles

Secondary-Type Mutations Do Not Impact the Favorable Outcome of NPM1 -Mutated Acute Myeloid Leukemia Patients - Results from a Large Cohort of Intensively Treated Patients

Blood (2023) 142 (Supplement 1): 721.

https://doi.org/10.1182/blood-2023-186160

Published: 2023

Journal Articles

Automated Preselection of Regions of Interest By Deep Learning Facilitates Rapid Whole Slide Image Analysis of Bone Marrow Smears

Martin M. K. Schneider, Freya Schulze, Tim Schmittmann, Sebastian Riechert, Miriam Eva Helena Gediga, Katja Sockel, Frank P. Kroschinsky, Karsten Wendt, Martin Bornhaeuser, Jan Moritz Middeke, Jan-Niklas Eckardt

Blood (2023) 142 (Supplement 1): 3662.

https://doi.org/10.1182/blood-2023-187501

Published: 2023

Journal Articles

Explainable End-to-End Supervised Learning Identifies Myelodysplastic Neoplasms in Bone Marrow Smears

Jan-Niklas Eckardt, Tim Schmittmann, Freya Schulze, Sebastian Riechert, Martin M. K. Schneider, Lukas Reichel, Miriam Eva Helena Gediga, Katja Sockel, Anas Shekh Sulaiman, Ekaterina Balaian, Christoph Röllig, Frank P. Kroschinsky, Anne Marie Asemissen, Uwe Platzbecker, Christian Pohlkamp, Torsten Haferlach, Karsten Wendt, Martin Bornhäuser, Jan Moritz Middeke

Blood (2023) 142 (Supplement 1): 2269.

https://doi.org/10.1182/blood-2023-185628

Published: 2023

Journal Articles

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence

Jan-Niklas Eckardt, Waldemar Hahn, Christoph Röllig, Sebastian Stasik, Uwe Platzbecker, Carsten Müller-Tidow, Hubert Serve, Claudia D Baldus, Christoph Schliemann, Kerstin Schäfer-Eckart, Maher Hanoun, Martin Kaufmann, Andreas Burchert, Christian Thiede, Johannes Schetelig, Martin Bornhäuser, Markus Wolfien, Jan Moritz Middeke

Blood (2023) 142 (Supplement 1): 2268.

https://doi.org/10.1182/blood-2023-179817

Published: 2023

Journal Articles

Prediction of Complete Remission and Survival in Acute Myeloid Leukemia Using Supervised Machine Learning

Blood (2021) 138 (Supplement 1): 108.

https://doi.org/10.1182/blood-2021-149582

Published: 2021

NARROW

Synthetic Bone Marrow Smears Are a Privacy-Preserving Substitute for Developing Accurate Leukemia Classification Models in Hematological Microscopy

Autonomous Region-of-Interest Detection, Cell Segmentation and Cell Classification in Bone Marrow Cytomorphology Using Deep Learning

Age-Stratified Game-Theory-Informed Machine Learning of Molecular Alterations Unveils Prognostic Divergence in 3062 Pediatric and Adult Acute Myeloid Leukemia Patients

The Differential Impact of Secondary-Type Mutations in a Multinational Cohort of 5311 Intensively Treated Acute Myeloid Leukemia Patients

Deep Learning Predicts Response to Venetoclax and Azacitidine in Patients with Newly Diagnosed Acute Myeloid Leukemia Solely Based on Bone Marrow Smear Image Data

Supervised Machine Learning Predicts Transformation Risk and Key Features throughout the Patient Journey in Myelodysplastic Neoplasms

Secondary-Type Mutations Do Not Impact the Favorable Outcome of NPM1 -Mutated Acute Myeloid Leukemia Patients - Results from a Large Cohort of Intensively Treated Patients

Automated Preselection of Regions of Interest By Deep Learning Facilitates Rapid Whole Slide Image Analysis of Bone Marrow Smears

Explainable End-to-End Supervised Learning Identifies Myelodysplastic Neoplasms in Bone Marrow Smears

Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence

Prediction of Complete Remission and Survival in Acute Myeloid Leukemia Using Supervised Machine Learning

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American Society of Hematology

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ASH Publications

American Society of Hematology

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