74 citations · 192 across the 36 of their papers we have counts for
6 papers · 1 filter
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Valentin Koch, Sophia J. Wagner, Salome Kazeminia +5
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in perip…
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
Michael Deutges, Ario Sadafi, Nassir Navab +1
Diagnosis of hematological malignancies depends on accurate identification of white blood cells in peripheral blood smears. Deep learning techniques are emerging as a viable soluti…
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann +10
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniq…
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Xudong Sun, Nutan Chen, Alexej Gossmann +8
A probabilistic graphical model is proposed, modeling the joint model parameter and multiplier evolution, with a hypervolume based likelihood, promoting multi-objective descent in…
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification
Salome Kazeminia, Max Joosten, Dragan Bosnacki +1
Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training. Diseases like Acute Myeloid…
Low-resource finetuning of foundation models beats state-of-the-art in histopathology
Benedikt Roth, Valentin Koch, Sophia J. Wagner +3
To handle the large scale of whole slide images in computational pathology, most approaches first tessellate the images into smaller patches, extract features from these patches, a…