Lawrence D. True

SCOPE improves AI triage in 3D pathology by using morphology-aware prototypes to identify high-risk 2D slices for pathologist review.

SCALE3D improves prostate cancer risk prediction by analyzing 3D cell interactions through supercell graphs, outperforming traditional features and reducing computational demands.

This paper introduces a 3D microdissection method combining CNC milling and light-sheet microscopy to study tumor evolution in complex 3D structures.

This paper develops a 3D computational pipeline called SIGHT to detect prostate cancer in pathology datasets using generative immunolabeling, achieving an F1 score comparable to pathologist agreement.

CARP3D uses deep learning to prioritize high-risk 2D sections in 3D pathology data, improving disease detection while reducing pathologist workload.

The paper uses single-cell analysis to study how cell surface antigen expression changes in treatment-resistant prostate cancer, revealing transcriptional heterogeneity and shared gene regulatory networks between NEPC and SCLC.

This study uses PS-OCT to differentiate bone tissue types in prostate cancer metastases by analyzing collagen organization and quantifying structural features.