Journal ArticleDOI
Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.
TLDR
The use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis are covered.About:
This article is published in Translational Research.The article was published on 2017-11-07. It has received 197 citations till now. The article focuses on the topics: Breast cancer.read more
Citations
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Journal ArticleDOI
Artificial intelligence and digital pathology: Challenges and opportunities
TL;DR: This paper strives to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.
Book ChapterDOI
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
TL;DR: Raghlin et al. as mentioned in this paper developed a computational approach based on deep convolution neural networks for breast cancer histology image classification, which utilizes several deep neural network architectures and gradient boosted trees classifier.
Journal ArticleDOI
Artificial intelligence as the next step towards precision pathology.
TL;DR: The latest developments in digital image analysis and in the application of artificial intelligence in diagnostic pathology are presented and summarized.
Journal ArticleDOI
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Siddique Latif,Muhammad Usman,Sanaullah Manzoor,Waleed Iqbal,Junaid Qadir,Gareth Tyson,Ignacio Castro,Adeel Razi,Maged N. Kamel Boulos,Adrian Weller,Jon Crowcroft +10 more
TL;DR: This paper attempts to systematise the various COVID-19 research activities leveraging data science, where data science is defined broadly to encompass the various methods and tools that can be used to store, process, and extract insights from data.
Journal ArticleDOI
Tutorial: guidance for quantitative confocal microscopy.
TL;DR: This tutorial and the accompanying poster provide a guide for performing quantitative fluorescence imaging using confocal microscopy, including advice and troubleshooting information from sample preparation and microscope setup to data analysis and statistics.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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