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Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images2025 IEEE International Symposium on Biomedical Imaging (ISBI) conference-paper Computer Science AI in cancer detection Open access
Fatemeh Javadian, Zahra Aminparast, Johannes Stegmaier, Abin Jose
0citations -
Surrounding Cell Suppression For Unsupervised Representation Learning In Hematological Cell Classification2021 IEEE International Symposium on Biomedical Imaging (ISBI) conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Ina Laube, Martina Margrit Crysandt, Reinhild Herwartz, Melanie Baumann, Barbara M. Klinkhammer, +3 more
0citations -
Analysis of automatically generated embedding guides for cell classification2022 International Conference on Image Processing Theory, Tools and Applications (IPTA) conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Julian Thull, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, +1 more
0citations -
Ordinal Classification and Regression Techniques for Distinguishing Neutrophilic Cell Maturity Stages in Human Bone Marrow2022 Lecture notes in computer science conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, Dorit Merhof
0citations -
Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study2024 Journal of Investigative Dermatology article Computer Science AI in cancer detection Open access
Thom Doeleman, Siemen Brussee, Liesbeth M. Hondelink, Daniëlle W F Westerbeek, Ana Sequeira, P. Valkema, +8 more
7citations -
The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks2020 PLoS Computational Biology article Computer Science Neural Networks and Applications Open access
Matthieu Gilson, David Dahmen, Rubén Moreno‐Bote, Andrea Insabato, Moritz Helias
11citations -
Automatic Embedding Interventions for the Classification of Hematopoietic Cells2022 Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Julian Thull, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, +1 more
0citations -
State of the Art Cell Detection in Bone Marrow Whole Slide Images2021 Journal of Pathology Informatics article Computer Science Digital Imaging for Blood Diseases Open access
Philipp Gräbel, Martina Margrit Crysandt, Reinhild Herwartz, Melanie Baumann, Barbara M. Klinkhammer, Peter Boor, +2 more
7citations -
Guided Representation Learning for the Classification of Hematopoietic Cells2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, Dorit Merhof
2citations -
Tree-based learning on amperometric time series data demonstrates high accuracy for classification2024 International Journal of Data Science and Analytics article Computer Science Time Series Analysis and Forecasting Open access
Jeyashree Krishnan, Zeyu Lian, Pieter E. Oomen, Mohaddeseh Amir-Aref, Xiulan He, Soodabeh Majdi, +2 more
2citations -
Spatial Maturity Regression for the Classification of Hematopoietic Cells2022 International Conference on Image Processing Theory, Tools and Applications (IPTA) conference-paper Computer Science Digital Imaging for Blood Diseases
Philipp Gräbel, Julian Thull, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, +1 more
0citations