2 citations · 2 across the 5 of their papers we have counts for
5 papers
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
Michael Deutges, Chen Yang, Raheleh Salehi +3
The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell cou…
Continual Multiple Instance Learning for Hematologic Disease Diagnosis
Zahra Ebrahimi, Raheleh Salehi, Nassir Navab +2
The dynamic environment of laboratories and clinics, with streams of data arriving on a daily basis, requires regular updates of trained machine learning models for consistent perf…
Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder
Gizem Mert, Ario Sadafi, Raheleh Salehi +2
Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combining morphological and transcriptom…
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Ario Sadafi, Raheleh Salehi, Armin Gruber +4
Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and…
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Raheleh Salehi, Ario Sadafi, Armin Gruber +4
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures,…