Tingying Peng

BaSiCPy improves illumination correction in microscopy images by addressing foreground correlations and enabling automated processing.

MemBrain v2 couples a generalist U-Net cryo-ET membrane segmenter, a mesh-based picker reaching 91% F1 for photosystem II from one annotated membrane, and geodesic particle statistics.

Phoenix predicts spatially resolved single-cell gene expression from histology, identifying new biomarkers and validating them across cancers and treatment regimens.

This study introduces ASCT to assess bacterial killing in real-time and links drug tolerance to clinical outcomes in mycobacterial infections.

Leonardo corrects sample-induced artifacts in light sheet microscopy images using two modules, validated on diverse biological samples.

This study uses cryo-ET and AI-based methods to analyze thylakoid membrane organization in spinach chloroplasts, revealing similarities to green algae despite differences in 3D architecture.

This paper introduces a Mean Shift loss function for more accurate protein localization in cryo-electron tomography using scoremap-guided clustering and random point sampling.

The paper introduces BEN, a deep learning method for brain tissue extraction across species and MRI modalities, showing improved accuracy and generalizability over existing tools.

MemBrain detects membrane proteins in cryo-ET data using a deep learning pipeline, achieving high accuracy with minimal labels and generalizing across different cell types and imaging methods.

This paper introduces DeepFinder, a deep learning method that improves macromolecule identification in 3D cellular cryo-ET data by localizing multiple species with high accuracy.