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

Artificial Video Generation for Improved Performance on Polyp Detection

Colorectal cancercolorectal cancer (CRC) is a widespread disease which is a threat to public health. Abnormalities in the colon, like polyps, can become cancerous. It is important to detect polyps early, in order to prevent a potential spread of cancer. Polyps can be overlooked during screening, and typically doctors have a polyp miss rate ranging from 14 to 30%. Several promising computer systems have been developed to help doctors lower their polyp miss rate. Obtaining a large, high quality dataset is important when building such a system, and the lack of data is perhaps the biggest challenge in the field today. Data is arguably the most valuable resource in machine learning. Complex neural networks are dependent on great amounts of data in order to perform well. The colon is full of complicated structures, and a dataset should contain examples of as many examples of both healthy and unhealthy structures as possible. However, medical data is hard to get hold of due to legal restrictions and the cost of performing examinations. Currently a highly qualified, medical expert is needed to annotate data as well, further complicating matters. We have developed a system which can take an existing dataset and use it to generate new, artificial data which can be added to the dataset. This will make it easier to create a large enough dataset for polyp detection systems. In other words, we can generate real-looking videos of polyps. A total of 41 generated videos was provided to two medical experts, and they were asked to comment on the quality of the videos. Their comments revealed that shapes and colors in the videos look real. They additionally stated that they found these videos relevant for detecting other abnormalities in the colon. We also trained two polyp classifiers on the same dataset, but for one of the classifiers we also added our artificial videos. We found that the results were inconclusive, though we believe that it should be possible for the artificial videos to improve performance

Action Recognition in Real Homes using Low Resolution Depth Video Data

We report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting and storing data from real homes, and preliminary results on action recognition using low resolution depth video cameras. The data have been collected from seven apartments with one resident each over a period of two weeks. We propose a pre-processing of the depth videos by applying an Infinite Response Filter (IIR) for extracting the movements in the frames prior to classification. In this work we classify four actions: TV interaction (turn it on/ off and switch over), standing up, sitting down, and no movement. Our first results indicate that using the IIR filter for movement information extraction improves accuracy and can be an efficient method for recognizing actions. Our current implementation uses a convolutional long short-term memory (ConvLSTM) neural network, and achieved an average peak accuracy of 86%

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