Detecting Prostatic Adenocarcinoma From Digitized Histology Using a Multi-Scale Hierarchical Classification Approach. - Scott Doyle; Carlos Rodriguez; Anant Madabhushi; John Tomaszewski 001; Michael D. Feldman (EMBC, 2006)

A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology. - Scott Doyle; Anant Madabhushi; Michael D. Feldman; John Tomaszewski 001 (MICCAI (2), 2006)

Automated Grading of Prostate Cancer Using Architectural and Textural Image Features. - Scott Doyle; Mark I. Hwang; Kinsuk Shah; Anant Madabhushi; Michael D. Feldman; John Tomaszewski 001 (ISBI, 2007)

Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features. - Scott Doyle; Shannon Agner; Anant Madabhushi; Michael D. Feldman; John Tomaszewski 001 (ISBI, 2008)

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. - Shivang Naik; Scott Doyle; Shannon Agner; Anant Madabhushi; Michael D. Feldman; John Tomaszewski 001 (ISBI, 2008)

A Knowledge Representation Framework for Integration, Classification of Multi-Scale Imaging and Non-Imaging Data: Preliminary Results in Predicting Prostate Cancer Recurrence by Fusing Mass Spectrometry and Histology. - George Lee 002; Scott Doyle; James Monaco; Michael D. Feldman; Stephen R. Master; John Tomaszewski 001; Anant Madabhushi (ISBI, 2009)

A boosted distance metric: application to content based image retrieval and classification of digitized histopathology. - Jay Naik; Scott Doyle; Ajay Basavanhally; Shridar Ganesan; Michael D. Feldman; John Tomaszewski 001; Anant Madabhushi (Computer-Aided Diagnosis, 2009)

WERITAS: weighted ensemble of regional image textures for ASM segmentation. - Robert Toth; Scott Doyle; Mark Rosen; Arjun Kalyanpur; Sona Pungavkar; B. Nicolas Bloch; Elizabeth Genega; Neil Rofsky; Robert E. Lenkinski; Anant Madabhushi (Image Processing, 2009)

Predicting classifier performance with a small training set: applications to computer-aided diagnosis and prognosis. - Ajay Basavanhally; Scott Doyle; Anant Madabhushi (ISBI, 2010)

Evaluation of effects of jpeg2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology. - Scott Doyle; James Monaco; Anant Madabhushi; Stefan Lindholm; Patric Ljung; Lance Ladic; John Tomaszewski 001; Michael D. Feldman (ISBI, 2010)

Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis. - Anant Madabhushi; Ajay Basavanhally; Scott Doyle; Shannon Agner; George Lee 002 (ISBI, 2010)

Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis. - Scott Doyle; Anant Madabhushi (PRIB, 2010)

Cascaded multi-class pairwise classifier (CascaMPa) for normal, cancerous, and cancer confounder classes in prostate histology. - Scott Doyle; Michael D. Feldman; John Tomaszewski 001; Natalie Shih; Anant Madabhushi (ISBI, 2011)

Boosted Spectral Embedding (BoSE): Applications to content-based image retrieval of histopathology. - Akshay Sridhar; Scott Doyle; Anant Madabhushi (ISBI, 2011)

An Active Learning Based Classification Strategy for the Minority Class Problem: Application to Histopathology Annotation. - Scott Doyle; James Monaco; Michael D. Feldman; John Tomaszewski 001; Anant Madabhushi (BMC Bioinform., 2011)

Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. - Anant Madabhushi; Shannon Agner; Ajay Basavanhally; Scott Doyle; George Lee 002 (Comput. Medical Imaging Graph., 2011)

Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. - Scott Doyle; Michael D. Feldman; Natalie Shih; John Tomaszewski 001; Anant Madabhushi (BMC Bioinform., 2012)

A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies. - Scott Doyle; Michael D. Feldman; John Tomaszewski 001; Anant Madabhushi (IEEE Trans. Biomed. Eng., 2012)

Mentor's brain functional connectivity network during robotic assisted surgery mentorship. - Somayeh B. Shafiei; Scott Doyle; Khurshid A. Guru (EMBC, 2016)

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

Data-driven sampling method for building 3D anatomical models from serial histology. - Snehal Ulhas Salunke; Tova Ablove; Theresa Danforth; John Tomaszewski 001; Scott Doyle (Digital Pathology, 2017)

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem. - Metin N. Gurcan; John Tomaszewski 001; James A. Overton; Scott Doyle; Alan Ruttenberg; Barry Smith 001 (J. Biomed. Informatics, 2017)

The Implementation of the Precision Oncology Data Repository in the Veterans Affairs Healthcare System. - Danne C. Elbers; Frank Meng; Scott Doyle; Sylvester Sakilay; Sung Feng-Chi; Brett R. Johnson; Robert B. Hall; Nathanael Fillmore; Daniel Chen; Alexander D. Diehl; Samuel Ajjarapu; Karen E. Pierce-Murray; et al. (AMIA, 2018)

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)

Role of training data variability on classifier performance and generalizability. - Ryan Therrien; Scott Doyle (Digital Pathology, 2018)

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. - Andrew Janowczyk; Scott Doyle; Hannah Gilmore; Anant Madabhushi (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2018)

Workflow Pipeline for Medical Image Data Curation and Sharing. - Sylvester Sakilay; Danne C. Elbers; Scott Doyle; Luis E. Selva; Brett R. Johnson; Nhan V. Do; Peter L. Elkin (AMIA, 2019)

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)

Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy. - Gouthamrajan Nadarajan; Tyna A. Hope; Dan Wang; Alison M. Cheung; Fiona Ginty; Martin J. Yaffe; Scott Doyle (Digital Pathology, 2019)

Two-tier classifier for identifying small objects in histological tissue classification: experiments with colon cancer tissue mapping. - Chen-Yu Sun; Weiguo Liu; Scott Doyle (Digital Pathology, 2019)

Realistic cross-domain microscopy via conditional generative adversarial networks: converting immunofluorescence to hematoxylin and eosin. - Gouthamrajan Nadarajan; Scott Doyle (Digital Pathology, 2020)

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)

Conditional generative adversarial networks for H&E to IF domain transfer: experiments with breast and prostate cancer. - Gouthamrajan Nadarajan; Scott Doyle (Digital Pathology, 2021)

Generating statistical shape models of osteological structure from cadaveric CT data. - Matthew A. Wysocki; Scott Doyle (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2022)

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)

Advancing finite element analysis of osteological structures in biomedical research. - Matthew A. Wysocki; Scott Doyle (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2023)

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

Attention-guided single-voxel attacks for 3-dimensional neural networks: experiments with post-mortem CT segmentation. - Steven A. Lewis; Scott Doyle (Image Perception, Observer Performance, and Technology Assessment, 2023)