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Proceedings, non-refereed
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
In 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021). Vol. 2886. CEUR Workshop Proceedings, 2021.Status: Published
Improving generalizibilty in polyp segmentation using ensemble convolutional neural network
Medical image segmentation is a crucial task in medical image analysis. Despite near expert-label performance with the application of the deep learning method in medical image segmentation, the generalization of such models in the clinical environment remains a significant challenge. Transfer learning from a large medical dataset from the same domain is a common technique to address generalizability. However, it is difficult to find a similar large medical dataset. To address generalizability in polyp segmentation, we have used an ensemble of four MultiResUNet architectures, each trained on the combination of the different centered datasets provided by the challenge organizers. Our method achieved a decent performance of 0.6172 ± 0.0778 for the multi-centered dataset. Our study shows that significant work needs to be done to develop a computer-aided diagnosis system to detect and localize polyp of the multi-center datasets, which is essential for improving the quality of the colonoscopy.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, non-refereed |
| Year of Publication | 2021 |
| Conference Name | 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021) |
| Volume | 2886 |
| Publisher | CEUR Workshop Proceedings |
| Keywords | colonoscopy, Convolutional neural network, health informatics, Polyp segmentation |
Automated Polyp Segmentation in Colonoscopy using MSRFNet
In MediaEval medico . CEUR Workshop Proceedings, 2021.Status: Published
Automated Polyp Segmentation in Colonoscopy using MSRFNet
Colorectal cancer is one of the major cause of cancer-related death around the world. High-quality colonoscopy is considered mandatory for resecting and preventing colorectal cancers. In the recent past, various technological advances have been made towards improving the quality of colonoscopy. Despite the technical advancement, some polyps are frequently missed during colonoscopy examinations. Polyp detection ( for example, adenomas) rates are largely influenced by inter-endoscopist variability. Therefore, it is very challenging to standardize a high-quality colonoscopy. A computer-aided detection system could solve the problem with miss-detection. The ``MediaEval 2021'' challenge entails the chance to study and develop accurate automated polyp segmentation algorithms \cite{Hicks2021Medico}. In this paper, we propose our approach based on MSRFNet. Our experimental findings show that the model trained on the Kvasir-SEG dataset and evaluated on a competition test dataset obtains a dice coefficient of 0.7055, Jaccard of 0.6176, a recall of 0.7293, and a precision of 0.7769. In addition to the MediaEval 2021 challenge, we evaluated our approach on the Endotect Challenge Dataset and ``2020 Medico Automatic Polyp Segmentation Challenge Dataset". The results further demonstrate the efficiency of our approach.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, non-refereed |
| Year of Publication | 2021 |
| Conference Name | MediaEval medico |
| Publisher | CEUR Workshop Proceedings |