Bastian Rieck

TopoMIL improves multiple instance learning for diagnostic microscopic images by integrating topological structures, achieving classification gains across various pooling methods.

The paper decodes stimulus identity and network state from calcium imaging in mice across different anesthesia states, showing preserved stimulus information even during deep anesthesia.

The paper shows that human hippocampal neurons encode visual sequences through population-level ring topologies that preserve temporal order.

Neurospectrum extracts interpretable spatiotemporal features from neural activity using a geometric and topological deep learning framework, outperforming traditional methods in simulations and fMRI data.

The paper improves antimicrobial resistance prediction using MALDI-TOF MS by incorporating phylogenetic structure through hierarchical clustering, increasing average precision for specific resistance predictions.

A topological machine learning approach applied to single-nucleus RNA sequencing identifies shared glial activation states in AMD, AD, and MS.