5 papers · 1 filter
Attention Pooling Enhances NCA-based Classification of Microscopy Images
Chen Yang, Michael Deutges, Jingsong Liu +4
Neural Cellular Automata (NCA) offer a robust and interpretable approach to image classification, making them a promising choice for microscopy image analysis. However, a performan…
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…
CytoSAE: Interpretable Cell Embeddings for Hematology
Muhammed Furkan Dasdelen, Hyesu Lim, Michele Buck +3
Sparse autoencoders (SAEs) emerged as a promising tool for mechanistic interpretability of transformer-based foundation models. Very recently, SAEs were also adopted for the visual…
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…
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…