Biology subjects

Rieck, B.

Publications and source records attributed to Rieck, B..

4 recordsLinked to original sources

bioRxiv2026

TopoMIL: Topology Improves Multiple Instance Learning in Diagnostic Microscopic Images

Microscopic images of cells and tissues are central to disease diagnosis. In computational pathology, multiple instance learning (MIL) has emerged as a key paradigm for analyzing numerous images within a single patient sample. While the representative distribution of cells in a sample is important for diagnosis, existing MIL frameworks largely overlook it. We introduce TopoMIL, a framework that extracts the representative topological structure of the sample and integrates it into the MIL classifier. Three topological representations are assessed, each with distinct advantages and computational costs. We evaluate TopoMIL on four histopathology and cytomorphology datasets, each presenting unique challenges. Integrating the samples topological information into MIL enhances classification across average, max, attention-based, and transformer pooling, yielding AUCROC gains of 3.3%, 4.2%, 5.9%, and 0.5%, respectively, with moderate computational cost. Our work underscores the potential of TopoMIL as a scalable extension to existing morphology-based models in computational pathology.

bioinformatics↗

bioRxiv2025

Representation of visual sequences in the tuning and topology of neuronal activity in the human hippocampus

Time is a critical component of memory and yet how the hippocampus incorporates temporal information with the sensory contents of memories remains unclear. We hypothesized that the hippocampus can learn arbitrary sequences through rapid changes in the tuning and population geometry of individual neurons to mirror the sequence structure. We recorded single-unit activity from 134 neurons in the hippocampus of 17 patients with epilepsy while they viewed sequences of visual scenes that were presented repeatedly in the same order by looping from the end to start. As the sequence repeated, hippocampal neurons that were initially responsive to one scene in the sequence began responding to the other scenes proportional to their temporal proximity. The population vectors of spiking activity across recorded hippocampal neurons for each scene came to form a high-dimensional ring topology that encoded the circular structure of the sequence in a manner that preserved the serial order of the scenes. These effects were not observed when sequences were scrambled upon each repetition to destroy temporal structure, nor in brain regions outside of the hippocampus for both structured and random sequences. These findings suggest that temporal structure governs the representation of sensory stimuli in human hippocampal neurons.

neuroscience↗

bioRxiv2023

Capturing Spatiotemporal Signaling Patterns in Cellular Data with Geometric Scattering Trajectory Homology

Neural signals are high-dimensional, noisy, and dynamic, making it challenging to extract interpretable features linked to behavior or disease. We introduce Neurospectrum, a framework that encodes neural activity as latent trajectories shaped by spatial and temporal structure. At each timepoint, signals are represented on a graph capturing spatial relationships, with a learnable attention mechanism highlighting important regions. These are embedded using graph wavelets and passed through a manifold-regularized autoencoder that preserves temporal geometry. The resulting latent trajectory is summarized using a principled set of descriptors - including curvature, path signatures, persistent homology, and recurrent networks -that capture multiscale geometric, topological, and dynamical features. These features drive downstream prediction in a modular, interpretable, and end-to-end trainable framework. We evaluate Neurospectrum on simulated and experimental datasets. It tracks phase synchronization in Kuramoto simulations, reconstructs visual stimuli from calcium imaging, and identifies biomarkers of obsessive-compulsive disorder in fMRI. Across tasks, Neurospectrum uncovers meaningful neural dynamics and outperforms traditional analysis methods.

bioinformatics↗

bioRxiv2020

Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning

Early administration of effective antimicrobial treatments is critical for the outcome of infections. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques take up to 72 hours. We have developed a novel machine learning approach to predict antimicrobial resistance directly from MALDI-TOF mass spectra profiles of clinical samples. We trained calibrated classifiers on a newly-created publicly available database of mass spectra profiles from clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. The dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation against a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, resulting in AUROC values of 0.8, 0.74, and 0.74 respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study found that implementation of this approach would have resulted in a beneficial change in the clinical treatment in 88% (8/9) of cases. MALDI-TOF mass spectra based machine learning may thus be an important new tool for antibiotic stewardship.

microbiology↗