Convolutional Neural Networks

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
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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
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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
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