Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Trade-offs of Adapting Binary Neural Network Ensembles for Multiclass Problems

The One Versus Rest (OVR) method of building classifiers has fallen out of favor with machine learning researchers in recent years. When used, it is mostly only with older binary linear classifiers such as Support Vector Machines. We suspected that one could also improve the classification performance of more complex neural network architectures by setting them up as individual binary classifiers for each class and combining the output. In our research, we have tested the OVR style of building a classifier on several modern neural network architectures, including DenseNet, Inception v3, Inception ResNet v2, Xception, NASNet, and MobileNet. We have compared several aspects of their performance during training and testing, in both an OVR style and conventional multiclass single-network style. We have compared hardware resource use, classification speed, and several classification accuracy metrics. Also, we trained and tested a total of 186 networks; 50 of these were multiclass, and 136 were individual binary networks. Using our final selection of 99 networks (11 multiclass and 88 binary), we compared the results of the 11 multiclass networks with the 11 OVR style networks built from the 88 individual binary classifiers using the Kvasir v2 dataset, which contains thousands of classified frames from colonoscopy videos. We chose The Kvasir dataset as it could provide a good indication of how applicable our method is to clinical research. We found that overall, there was a substantial increase in the average and median classification metrics when using our methods and applications. Using the OVR style resulted in a 7% increase in average F1 score, a ~1% increase in average accuracy, a 6% increase in Matthews Correlation Coefficient, a 1% increase in precision and finally a 4% increase in average recall. Specificity remained mostly unchanged. Also, the median values for all of these metrics increased significantly in the OVR style, with the median F1, MCC and recall scores increasing by over ~15%. The most improved network in the OVR configuration saw an increase in F1 of 45% and an MCC increase of 40%. However, on average our OVR multi-network style was 7.6 times slower to classify than a single network multiclass implementation. These collective findings lead us to conclude that the OVR method can be applied to modern neural networks structures, and will often result in increased classification accuracy, but at the cost of classification speed

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Protocol and description of literature searches for the risk-benefit assessment of fish in the Norwegian diet. The Steering Committee of the Norwegian Scientific Committee for Food and Environment

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Benefit and risk assessment of fish in the Norwegian diet - Scientific Opinion of the Steering Committee of the Norwegian Scientific Committee for Food and Environment

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