Image Processing

Deep neural networks have achieved excellent cell or nucleus quantification performance in microscopy images, but they often suffer …

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.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by …

Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to …

Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, …

PURPOSE: We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry …

OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 …

OBJECTIVES: To determine how high-resolution transrectal ultrasound (HiTRUS) compares with conventional TRUS (LoTRUS) for the …

Digital pathology systems offer pathologists an alternate, emerging mechanism to manage and interpret information. They offer …

OBJECTIVE: To analyze tissue microarrays (TMAs) using color deconvolution, a method for separating component dyes in digital images, …