6 citations · 6 across the 6 of their papers we have counts for
9 papers
Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction
Oytun Demirbilek, Tingying Peng, Alaa Bessadok
A morphological brain graph depicting a connectional fingerprint is of paramount importance for charting brain dysconnectivity patterns. Such data often has missing observations du…
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…
B-Cos Aligned Transformers Learn Human-Interpretable Features
Manuel Tran, Amal Lahiani, Yashin Dicente Cid +7
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology. However, domain experts are still reluctant to use these models d…
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Tomáš Chobola, Gesine Müller, Veit Dausmann +4
Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datas…
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Yu Liu, Gesine Muller, Nassir Navab +3
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light sca…
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Tomáš Chobola, Gesine Müller, Veit Dausmann +4
The process of acquiring microscopic images in life sciences often results in image degradation and corruption, characterised by the presence of noise and blur, which poses signifi…