2 citations · 4 across the 4 of their papers we have counts for
5 papers
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…
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…
Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios
Salome Kazeminia, Carsten Marr, Bastian Rieck
Multiple instance learning (MIL) is a framework for weakly supervised classification, where labels are assigned to sets of instances, i.e., bags, rather than to individual data poi…
Anomaly-aware multiple instance learning for rare anemia disorder classification
Salome Kazeminia, Ario Sadafi, Asya Makhro +3
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to…
GANs for Medical Image Analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper +4
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical…