Colorectal Cancer Outcome Prediction from H&amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Yue, Xingzhi; Dimitriou, Neofytos; Arandjelović, Ognjen

doi:10.48550/arxiv.1902.03582

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…In [138], a machine learning algorithm for predicting the prognosis of colorectal cancer from WSI is introduced. First, data preprocessing is performed, including chroma normalization, color block extraction, and data enhancement.…”

Section: Classification Methodsmentioning

confidence: 99%

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

Li¹,

Li²,

Li³

et al. 2021

Preprint

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In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed in segmentation, classification, and detection of histopathological WSI. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks (DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.

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Section: Classification Methodsmentioning

confidence: 99%

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

Li¹,

Li²,

Li³

et al. 2021

Preprint

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show abstract

“…They achieved high accuracy by using SVM as a classifier. Another study (Yue et al, 2019) used a modified VGG16 framework and SVM classifier on the WSI slides. This study received an F1-score of 70% and a phenomenal accuracy of 100%.…”

Section: Related Workmentioning

confidence: 99%

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Azar

Tounsi

Fati

et al. 2023

International Journal of Sociotechnology and Knowledge Development

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.

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“…Finally, to maximize the robustness and the generalizability of the learning process, we performed synthetic data augmentation. In particular, we increased the amount of training data by approximately 50% by adding images randomly rotated by ±25 • and reflected about the vertical or the horizontal axis [43]. Note that this kind of augmentation is particularly principled in the context of the present task because, unlike in the case of natural images wherein there is an inherent asymmetry in directions (e.g., the horizontal and vertical directions are objectively defined and cannot be swapped one for another), in the microscopy slides of interest here, all directions are interchangeable and in that sense equivalent.…”

Section: Layer Kernel Size Strides Paddingmentioning

confidence: 99%

Tuberculosis Bacteria Detection and Counting in Fluorescence Microscopy Images Using a Multi-Stage Deep Learning Pipeline

et al. 2022

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The manual observation of sputum smears by fluorescence microscopy for the diagnosis and treatment monitoring of patients with tuberculosis (TB) is a laborious and subjective task. In this work, we introduce an automatic pipeline which employs a novel deep learning-based approach to rapidly detect Mycobacterium tuberculosis (Mtb) organisms in sputum samples and thus quantify the burden of the disease. Fluorescence microscopy images are used as input in a series of networks, which ultimately produces a final count of present bacteria more quickly and consistently than manual analysis by healthcare workers. The pipeline consists of four stages: annotation by cycle-consistent generative adversarial networks (GANs), extraction of salient image patches, classification of the extracted patches, and finally, regression to yield the final bacteria count. We empirically evaluate the individual stages of the pipeline as well as perform a unified evaluation on previously unseen data that were given ground-truth labels by an experienced microscopist. We show that with no human intervention, the pipeline can provide the bacterial count for a sample of images with an error of less than 5%.

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Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Cited by 6 publications

References 31 publications

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Tuberculosis Bacteria Detection and Counting in Fluorescence Microscopy Images Using a Multi-Stage Deep Learning Pipeline

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