Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

Du, Wenju; Rao, Nini; Yong, Jiahao; Wang, Yingchun; Hu, Dingcan; Gan, Tao; Zhu, Linlin; Zeng, Bing

doi:10.1007/s10916-021-01782-z

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…Te past fve years witness a series of studies assessing the performance of DL in the diagnosis of esophageal diseases [16][17][18][19][20][21][22]. Te main application is the computer vision task, consisting of the detection and segmentation lesions in esophageal endoscopic images or video [23,24]. Te CAD system is designed to detect and diferentiate lesions based on the mucosal/ vascular pattern, to stratify the progression of the diseases or to assist the decision-making of therapy [20,25,26].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Zhou

Wang

et al. 2023

Journal of Healthcare Engineering

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This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew’s correlation coefficient (0.936 and 0.884), and Cohen’s kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Zhou

Wang

et al. 2023

Journal of Healthcare Engineering

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“…In the experimental analyses performed on the Kvasir dataset, which consists of a multi-class structure, 93.19% classification accuracy and 92.8% F1-score values were found. Du et al [ 27 ] developed a semi-supervised effective comparative learning classification architecture for esophageal disease. With this architecture, 92.57% accuracy was achieved in experimental studies.…”

Section: Related Workmentioning

confidence: 99%

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Demirbaş,

Üzen,

Fırat

2024

Health Inf Sci Syst

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Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture’s results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

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“…In the endoscopic scenario, few studies have been carried out using semi-supervised learning. Du et al [47] implemented a semisupervised contrastive learning method for Esophageal Disease Classification in a small dataset. Golhar et al [48] proposed the use an unsupervised jigsaw learning method for GI lesion classification obtaining an improvement in accuracy of 9.8% with respect to supervised methods.…”

Section: Semi-supervised Image Classificationmentioning

confidence: 99%

Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images

Lazo

Rosa²,

Catellani

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semisurprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data. The dataset is available at https://zenodo.org/record/7741476#.ZBQUK7TMJ6k

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Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

Cited by 14 publications

References 32 publications

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images

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