GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Xu, Sai; Wang, Long; Liu, Yu; Wu, Jian; Huang, Hong

doi:10.3389/fonc.2022.901475

Cited by 3 publications

(3 citation statements)

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“…In their study, Shi et al (30) researched to develop an automated method for identifying regions impacted by gastric cancer in photographs of gastric histopathology samples. The researchers employed a convolutional neural network (CNN) decoder in their methodology to extract relevant features and incorporate an attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images

Waseem Sabir,

Farhan,

Almalki

et al. 2023

Front. Med.

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Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

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Section: Related Workmentioning

confidence: 99%

FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images

Waseem Sabir,

Farhan,

Almalki

et al. 2023

Front. Med.

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“…A hierarchical feature aggregation structure is designed in the decoder, which can effectively fuse deep and shallow features. The attention feature fusion module is introduced to accurately locate the lesion area, and the attention features of different scales are fused to obtain rich lesion discrimination information ( 23 ).…”

Section: Introductionmentioning

confidence: 99%

RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning

Zuo

2023

Front. Oncol.

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ObjectiveDue to the small proportion of target pixels in computed tomography (CT) images and the high similarity with the environment, convolutional neural network-based semantic segmentation models are difficult to develop by using deep learning. Extracting feature information often leads to under- or oversegmentation of lesions in CT images. In this paper, an improved convolutional neural network segmentation model known as RAD-UNet, which is based on the U-Net encoder-decoder architecture, is proposed and applied to lung nodular segmentation in CT images.MethodThe proposed RAD-UNet segmentation model includes several improved components: the U-Net encoder is replaced by a ResNet residual network module; an atrous spatial pyramid pooling module is added after the U-Net encoder; and the U-Net decoder is improved by introducing a cross-fusion feature module with channel and spatial attention.ResultsThe segmentation model was applied to the LIDC dataset and a CT dataset collected by the Affiliated Hospital of Anhui Medical University. The experimental results show that compared with the existing SegNet [14] and U-Net [15] methods, the proposed model demonstrates better lung lesion segmentation performance. On the above two datasets, the mIoU reached 87.76% and 88.13%, and the F1-score reached 93.56% and 93.72%, respectively. Conclusion: The experimental results show that the improved RAD-UNet segmentation method achieves more accurate pixel-level segmentation in CT images of lung tumours and identifies lung nodules better than the SegNet [14] and U-Net [15] models. The problems of under- and oversegmentation that occur during segmentation are solved, effectively improving the image segmentation performance.

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“…Regarding pneumonia, many studies have considered the automatic detection of COVID-19 pneumonia [ 9 ] and predicted the severity of the disease [ 10 ]. In a study using non-lung histopathology specimens, Shi et al proposed a method for the automatic detection of gastric cancer regions in images of gastric histopathology specimens using a CNN decoder for feature extraction and an attention mechanism [ 11 ]. The evaluation using two datasets showed that it has a satisfactory detection agreement.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks

et al. 2022

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Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.

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GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Cited by 3 publications

References 50 publications

FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images

FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images

RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning

Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks

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