Pre-processing Techniques for Colour Digital Pathology Image Analysis

2017 ◽  
pp. 551-560 ◽  
Author(s):  
Wael Saafin ◽  
Gerald Schaefer
Keyword(s):  
Image Analysis ◽  
Digital Pathology ◽  
Processing Techniques ◽  
Pathology Image

Deep learning in digital pathology image analysis: a survey

2020 ◽  
Vol 14 (4) ◽  
pp. 470-487
Author(s):  
Shujian Deng ◽  
Xin Zhang ◽  
Wen Yan ◽  
Eric I-Chao Chang ◽  
Yubo Fan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Pathology Image

scholarly journals QuPath: Open source software for digital pathology image analysis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Peter Bankhead ◽  
Maurice B. Loughrey ◽  
José A. Fernández ◽  
Yvonne Dombrowski ◽  
Darragh G. McArt ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Digital Pathology ◽  
Pathology Image

scholarly journals Digital pathology image analysis: opportunities and challenges

2009 ◽  
Vol 1 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Anant Madabhushi
Keyword(s):  
Image Analysis ◽  
Digital Pathology ◽  
Pathology Image

The effect of quality control on accuracy of digital pathology image analysis

2020 ◽  
pp. 1-1
Author(s):  
Alexander Ian Wright ◽  
Catriona Marie Dunn ◽  
Michael Hale ◽  
Gordon Hutchins ◽  
Darren Treanor
Keyword(s):  
Quality Control ◽  
Image Analysis ◽  
Digital Pathology ◽  
Pathology Image

scholarly journals Novel chromaticity similarity based color texture descriptor for digital pathology image analysis

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206996 ◽  
Author(s):  
Xingyu Li ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Image Analysis ◽  
Digital Pathology ◽  
Texture Descriptor ◽  
Pathology Image ◽  
Color Texture

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

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