AIMC Topic: Tissue Array Analysis

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Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.

JAMA network open Mar 1, 2019
IMPORTANCE: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies.
Middle Aged Prostatic Neoplasms Algorithms Male Image Interpretation, Computer-Assisted Tissue Array Analysis Neoplasm Grading Prostate Humans Artificial Intelligence
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MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.

Proteomics. Clinical applications Dec 14, 2018
PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-a...
Machine Learning Female Humans Support Vector Machine Ovarian Neoplasms Middle Aged Proteomics Tissue Array Analysis Linear Energy Transfer Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Molecular Imaging Discriminant Analysis Carcinoma, Ovarian Epithelial
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An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

IEEE transactions on medical imaging Sep 3, 2018
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul...
Tissue Array Analysis Breast Neoplasms Deep Learning Image Interpretation, Computer-Assisted Immunohistochemistry Databases, Factual Female Humans Algorithms
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Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

Scientific reports Aug 13, 2018
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibili...
Reproducibility of Results Male Middle Aged Feasibility Studies Deep Learning Neoplasm Grading Models, Biological Cohort Studies Humans Prostate Survival Analysis Prostatic Neoplasms Prognosis Tissue Array Analysis
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Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.

Annals of biomedical engineering Jul 26, 2018
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor ...
Ovarian Neoplasms Tissue Array Analysis Humans Deep Learning Female Databases, Factual Image Processing, Computer-Assisted Breast Neoplasms
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Constrained Deep Weak Supervision for Histopathology Image Segmentation.

IEEE transactions on medical imaging Jul 7, 2017
In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS);...
Histocytochemistry Tissue Array Analysis Colon Colonic Neoplasms Humans Image Processing, Computer-Assisted Algorithms Supervised Machine Learning Neural Networks, Computer Databases, Factual
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