Classification of Polyps in Endoscopic Images Using Self-Supervised Structured Learning

Huang, Qi-Xian; Lin, Guo-Shiang; Sun, Hung–Min

doi:10.1109/access.2023.3277029

Cited by 3 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the HYPER-KVASIR dataset, Yue et al [25] used MobileNet-v2, Inception-v3, and PVTv2-B1, achieving accuracy scores above 90%, but precision and recall values were not provided. Huang et al [56] used their model to get an accuracy score of 87.1% while maintaining balanced precision and recall values on the Chang Bing Show Chwan Memorial Hospital dataset. Notably, the proposed method, an EfficientNet ensemble learning model on the PICCLO dataset, outperformed earlier studies with a remarkable accuracy of 94.25%.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Saad,

Maghraby,

Badawy

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment.

show abstract

Section: Comparison With Existing Methodsmentioning

confidence: 99%

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Saad,

Maghraby,

Badawy

2023

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Although SSL has demonstrated effectiveness in general computer vision tasks and certain aspects of gastrointestinal endoscopy, its specific application in the nuanced field of endoscopic classification, especially with the latest contrastive learning methods, is still an area ripe for exploration. The only study we are aware of in this area is by Huang et al 50 , which focused on using SimCLR 22 , an SSL method. This method maximizes agreement between differently augmented views of the same data instance in a latent space and requires an extremely large batch size to avoid collapsing.…”

Section: Related Workmentioning

confidence: 99%

Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning

Guo,

Somayajula,

Hosseini

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Endoscopy, a widely used medical procedure for examining the gastrointestinal (GI) tract to detect potential disorders, poses challenges in manual diagnosis due to non-specific symptoms and difficulties in accessing affected areas. While supervised machine learning models have proven effective in assisting clinical diagnosis of GI disorders, the scarcity of image-label pairs created by medical experts limits their availability. To address these limitations, we propose a curriculum self-supervised learning framework inspired by human curriculum learning. Our approach leverages the HyperKvasir dataset, which comprises 100k unlabeled GI images for pre-training and 10k labeled GI images for fine-tuning. By adopting our proposed method, we achieved an impressive top-1 accuracy of 88.92% and an F1 score of 73.39%. This represents a 2.1% increase over vanilla SimSiam for the top-1 accuracy and a 1.9% increase for the F1 score. The combination of self-supervised learning and a curriculum-based approach demonstrates the efficacy of our framework in advancing the diagnosis of GI disorders. Our study highlights the potential of curriculum self-supervised learning in utilizing unlabeled GI tract images to improve the diagnosis of GI disorders, paving the way for more accurate and efficient diagnosis in GI endoscopy.

show abstract

“…Polyps are benign or harmless but can become malignant if not detected in early stage. Polyps can be classified as hyperplastic (Hp) and Adenomatous (Ad) [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

View full text Add to dashboard Cite

Colorectal cancer has been one of the leading causes of mortality over the past decade, and colorectal polyps are the leading cause of this disease. Conventional polyp detection techniques are insufficient for proper detection; thus, an efficient detection method is indispensable. In this study, we collected colorectal images from a hospital in Taiwan, annotated the ground truth of polyp locations, and integrated them with a public dataset to create a colonoscopy dataset. Data augmentation techniques are further used to increase the training dataset's diversity to improve the models' detection performance. By developing the comparison system based on the recent state-of-the-art methods (i.e., FasterRCNN, SSD, YOLOv3, and YOLOv4), we compared the measurement metrics and statistically analyzed the performance of the models to identify the significant statistical difference in models' performance. Moreover, we developed and integrated error handling mechanism with each model to discard the false and null predictions. Finally, our model comparison system selects and proposes the best performing deep learning model to detect and classify colorectal polyps. We expect that the proposed model will accurately locate and classify different types of polyps. Eventually, this approach will ensure a valuable medical aid model. INDEX TERMSColorectal cancer (CRC), colorectal polyps, polyp detection, deep learning, data augmentation, error handling.

show abstract

Classification of Polyps in Endoscopic Images Using Self-Supervised Structured Learning

Cited by 3 publications

References 44 publications

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Contact Info

Product

Resources

About