Quantification of tumor morphology via 3D histology: application to oral cavity cancers. - Scott Doyle; Margaret Brandwein-Gensler; John Tomaszewski 001 (Digital Pathology, 2016)

Active deep learning: Improved training efficiency of convolutional neural networks for tissue classification in oral cavity cancer. - Jonathan Folmsbee; Xulei Liu; Margaret Brandwein-Weber; Scott Doyle (ISBI, 2018)

Fragile neural networks: the importance of image standardization for deep learning in digital pathology. - Jonathan Folmsbee; Starr Johnson; Xulei Liu; Margaret Brandwein-Weber; Scott Doyle (Digital Pathology, 2019)

Comparing architectural features between heuristically human-annotated and Artificial Intelligence (AI) generated tumor and satellite labels in early-stage oral cavity cancer. - Dhadma Balachandran; Margaret Brandwein-Weber; Jonathan Folmsbee; Scott Doyle (Digital Pathology, 2021)

Whole slide semantic segmentation: large scale active learning for digital pathology. - Jonathan Folmsbee; Margaret Brandwein-Weber; Scott Doyle (Digital Pathology, 2021)

Automatic flagging of AI segmentation errors in computational pathology. - Rakesh Choudhary; Dhadma Balachandran; Jonathan Folmsbee; Jawaria Rahman; Margaret Brandwein-Weber; Scott Doyle (Digital and Computational Pathology, 2022)

Cloud-based platform for human-in-the-loop re-annotation of whole slide imaging: large scale semantic pathology segmentation. - Jonathan Folmsbee; Scott Doyle; Rakesh Choudhary; Margaret Brandwein-Weber; Jawaria Rahman (Digital and Computational Pathology, 2022)

Combining multiple ground truth annotations for segmentation training for oral cavity cancer. - Jonathan Folmsbee; Margaret Brandwein-Weber; Scott Doyle (Digital and Computational Pathology, 2023)