CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images

Šarić, Matko; Russo, Mladen; Stella, Maja; Sikora, Marjan

doi:10.23919/splitech.2019.8783041

Cited by 79 publications

(19 citation statements)

References 12 publications

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“…To detect cancer cells and their stages. While Saric et al [18] proposed a fully automatic method for detecting lung cancer in lung tissue. They used two convolutional neural network CNN architectures (VGG and ResNet) for training, and their performance is compared.…”

Section: Introductionmentioning

confidence: 99%

Parallel extreme gradient boosting classifier for lung cancer detection

Abdualjabar

Awad

2021

IJEECS

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.

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Section: Introductionmentioning

confidence: 99%

Parallel extreme gradient boosting classifier for lung cancer detection

Abdualjabar

Awad

2021

IJEECS

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“…Unsupervised feature learning is an approach in which the system automatically learns and selects appropriate features from the image to maximize class separability. Convolutional neural network (CNN) [27] is a type of DL approach that has been used extensively in computational pathology models for a variety of tasks, including segmentation, object detection, and image classification [52,55,56,[61][62][63][64][65][66][67][68][69][70]. CNNs are composed of multiple layers of networks and are designed to learn spatial hierarchies of features automatically and adaptively through a backpropagation algorithm.…”

Section: Deep Learning (Dl)-based Unsupervised Feature Learningmentioning

confidence: 99%

The state of the art for artificial intelligence in lung digital pathology

Viswanathan

Toro

Corredor

et al. 2022

The Journal of Pathology

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Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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“…Considering the model generalizability, F-Net is designed as a standard residual neural network (ResNet), 29 which is a commonly used network architecture in various medical image classification tasks. 23,30,56,57 Compared to vanilla CNNs, ResNets apply shortcut connections between non-adjacent convolutional (Conv) layers in residual blocks, to address the gradient vanishing issue for deeper networks with more activation layers. 58 The architecture The following classifier is designed as an adaptive fuzzy evidential neuron network (AFENN) architecture.…”

Section: Network Architecturesmentioning

confidence: 99%

“…These CNN-based methods have achieved great successes in various medical imaging classification applications. 25,[30][31][32] However, DL-based methods can suffer from the overfitting problem caused by several data-related issues such as the limited amount of training data, data imbalance, and low inter-class reliability, 33 which are common problems of medical image data. Recently, generative adversarial network (GAN), 34 which was originally proposed as an image synthesis technique, has been increasingly adopted in image classification to help address the overfitting problem and improve the classification performance.…”

Section: Introductionmentioning

confidence: 99%

A novel adversarial learning strategy for medical image classification

Fan¹,

Zhang²,

Gasienica³

et al. 2022

Preprint

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Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and robustness. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification. Two main innovations were adopted in our AuxCNN classification framework. First, the proposed AuxCNN architecture includes an image generator and an image discriminator for extracting more informative image features for medical image classification, motivated by the concept of generative adversarial network (GAN)

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CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images

Cited by 79 publications

References 12 publications

Parallel extreme gradient boosting classifier for lung cancer detection

Parallel extreme gradient boosting classifier for lung cancer detection

The state of the art for artificial intelligence in lung digital pathology

A novel adversarial learning strategy for medical image classification

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