@inproceedings {39584,
	title = {Automated Polyp Segmentation in Colonoscopy using MSRFNet},
	journal = {MediaEval medico },
	year = {2021},
	publisher = {CEUR Workshop Proceedings},
	abstract = {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 {\textquoteleft}{\textquoteleft}MediaEval 2021{\textquoteright}{\textquoteright} 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\&nbsp; 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 {\textquoteleft}{\textquoteleft}2020 Medico Automatic Polyp Segmentation Challenge Dataset". The results further demonstrate the efficiency of our approach.\&nbsp;},
	author = {Rauniyar, Saurab and Srivastava, Abhishek and Vabesh Kumar {Jha} and Ritika Kumari {Jha} and Jha, Debesh and Rauniyar, Ashish}
}
@inproceedings {27636,
	title = {Automatic Polyp Segmentation using U-Net-ResNet50},
	journal = {Medico MediaEval 2020},
	year = {2021},
	publisher = {MediaEval},
	abstract = {Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The {\textquoteleft}{\textquoteleft}Medico automatic polyp segmentation challenge{\textquoteright}{\textquoteright} provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer{\textquoteright}s dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.},
	author = {Alam, Saruar and Nikhil Kumar {Tomar} and Thakur, Aarati and Jha, Debesh and Rauniyar, Ashish}
}
@article {39521,
	title = {Improving Polyp Segmentation in Colonoscopy using Deep Learning},
	journal = {Nordic Machine Intelligence},
	volume = {1},
	year = {2021},
	month = {11/2021},
	pages = {35-37},
	publisher = {Nordic Machine Intelligence},
	abstract = {Colorectal cancer is one of the major causes of cancer-related deaths globally. Although colonoscopy is considered as the gold standard for examination of colon polyps, there is a significant miss rate of around 22-28\%. Deep learning algorithms such as convolutional neural networks can aid in the detection and describe abnormalities in the colon that clinicians might miss during endoscopic examinations. The" MedAI: Transparency in Medical Image Segmentation" competition provides an opportunity to develop accurate and automated polyp segmentation algorithms on the same dataset provided by the challenge organizer. We participate in the polyp segmentation task of the challenge and provide a solution based on the dual decoder attention network (DDANet). The DDANet is an encoder-decoder-based architecture based on a dual decoder attention network. Our experimental results on the organizers{\textquoteright} dataset showed a dice coefficient of 0.7967, Jaccard index of 0.7220, a recall of 0.8214, a precision of 0.8359, and an accuracy of 0.9557. Our results on unseen datasets suggest that deep learning and computer vision-based methods can effectively solve automated polyp segmentation tasks.},
	author = {Rauniyar, Saurab and Vabesh Kumar {Jha} and Ritika Kumari {Jha} and Jha, Debesh and Rauniyar, Ashish}
}
