Computer-Assisted

Learning with limited target data to detect cells in cross-modality images
Deep neural networks have achieved excellent cell or nucleus quantification performance in microscopy images, but they often suffer …
Fuyong Xing, Xinyi Yang, Toby C. Cornish, Debashis Ghosh
Cite DOI
Quantitative Analysis of Ki67, a Prognostic Biomarker in Gastroenteropancreatic Neuroendocrine Tumors
Ki67 labeling index (LI) is the preferred method of establishing grade for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). There are a number of acceptable methods for calculating the Ki67 LI in GEP-NETs, but application of quantitative image analysis is a promising method to increase accuracy and repeatability. KiNet is a single-stage deep-learning-based detection and classification pipeline for Ki67 LI with performance meeting or exceeding the current state of the art.
May 19, 2022 2:00 PM — 3:00 PM MD Anderson Cancer Center (Virtual)
Toby C. Cornish
Quantitative Analysis of Ki67, a Prognostic Biomarker in Gastroenteropancreatic Neuroendocrine Tumors
Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images
Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by …
Fuyong Xing, Toby C. Cornish, Tellen D. Bennett, Debashis Ghosh
Cite DOI
Artificial intelligence for automating the measurement of histologic image biomarkers
Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to …
Toby C. Cornish
Cite DOI
Artificial intelligence for automating the measurement of histologic image biomarkers
Clinical Application of Image Analysis in Pathology
Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, …
Toby C. Cornish
Cite DOI
Clinical Application of Image Analysis in Pathology
Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67.
PURPOSE: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry …
Xuhong Zhang, Toby C. Cornish, Lin Yang, Tellen D. Bennett, Debashis Ghosh, Fuyong Xing
Cite DOI
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 …
Fuyong Xing, Toby C. Cornish, Tell Bennett, Debashis Ghosh, Lin Yang
Cite DOI
US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised
April 12, 2017 marked a significant day in the evolution of digital pathology in the United States, when the US Food and Drug …
Andrew J. Evans, Thomas W. Bauer, Marilyn M. Bui, Toby C. Cornish, Helena Duncan, Eric F. Glassy, Jason Hipp, Robert S. McGee, Doug Murphy, Charles Myers, Dennis G. O'Neill, Anil v. Parwani, B. Alan Rampy, Mohamed E. Salama, Liron Pantanowitz
Cite DOI
Radiologic-pathologic analysis of contrast-enhanced and diffusion- weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative image analysis
PURPOSE: To evaluate the diagnostic performance of three-dimensional ( 3D three-dimensional ) quantitative enhancement-based and …
Julius Chapiro, Laura D. Wood, MingDe Lin, Rafael Duran, Toby C. Cornish, David Lesage, Vivek Charu, Rudiger Schernthaner, Zhijun Wang, Vania Tacher, Lynn Jeanette Savic, Ihab R. Kamel, Jean-Francois Geschwind
Cite DOI
High-resolution transrectal ultrasound: pilot study of a novel technique for imaging clinically localized prostate cancer
OBJECTIVES: To determine how high-resolution transrectal ultrasound (HiTRUS) compares with conventional TRUS (LoTRUS) for the …
Christian P. Pavlovich, Toby C. Cornish, Jeffrey K. Mullins, Joel Fradin, Lynda Z. Mettee, Jason T. Connor, Adam C. Reese, Frederic B. Askin, Rachael Luck, Jonathan I. Epstein, Harry B. Burke
Cite DOI