Matthew Lee

This study decomposes scanner batch effects in pathology foundation models and shows that image corrections based on measured properties improve alignment but do not fully match model representations.

This study evaluates the limits of marker information transfer in cross-panel cytometry imputation, finding that imputation preserves broad lineage classification but not fine-phenotype details, and that patient matching improves cross-platform distribution recovery.

CytoGate-Bench evaluates LLMs for cross-panel cell gating in cytometry by reformulating manual gating as a zero-shot task using expert-annotated data.

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

The paper introduces cyMAE, a masked autoencoder for automated immunophenotyping, showing it improves accuracy and interpretability over manual methods in cytometry data analysis.

DeepCORE identifies co-operative regulatory elements by modeling genetic, epigenetic, and transcriptional patterns across 25 cell lines, outperforming existing methods and providing interpretable insights.