14 research outputs found
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
Working as a Live Streamer: The Effect of Audience Interaction on Psycho-Social Work Environment
Live-streaming er et nytt mediefenomen som har fremmet mange nye karrieremuligheter. Hensikten med denne studien er Ä utforske live-streamerenes arbeidsbetingelser og hvordan seerkravene pÄvirker deres arbeidsforhold. Basert pÄ 4 dybdeintervjuer brukte jeg en tematisk analyse for Ä undersÞke hva de unike arbeidsbetingelser til dette yrket var, og hvordan informantene ble pÄvirket av deres interaksjon og kommunikasjon med sitt publikum. I forbindelse med intervjuene ble offentlige tilgjengelig data relatert til streamerenes kringkastingsplaner analysert. Dette ble gjort for Ä fÄ en mer omfattende innsikt i arbeidsmengden deres. For det fÞrste brukte jeg arbeidskrav-ressurs teorien som et konseptuelt rammeverk for Ä identifisere arbeidsforholdene deres. For det andre, brukte jeg relevant teori for Ä utforske hvordan seerne pÄvirket de identifiserte arbeidsforholdene. Resultatene antyder at seerkrav fremmer en stor arbeidsmengde, dÄrlig work-life balance og et komplekst tap av autonomi. Samfunnene og seerne som samles rundt hver enkelt streamer, er i tillegg betydelige kilder til meningsskaping og sosial stÞtte. Dette motiverer streamerne til Ä fortsette med karrieren sin i dette yrket
Straff etter dyrevelferdsloven - den nedre grensen for kriminalisering etter dyrevelferdsloven § 3, jf. § 37
Dyrevelferdsloven § 3 er en generell aktsomhetsnorm som tar sikte pÄ Ä regulere alle tilfeller som faller utenfor ordlyden i andre materielle bestemmelser i dyrevelferdsloven. Straffansvar for overtredelser av bestemmelsen kan idÞmmes etter § 3, jf. § 37. Det oppstÄr spÞrsmÄl om ordlyden i § 3 er i trÄd med presisjonskravet i Grl. § 96 og EMK art. 7 og om den i tilstrekkelig grad tilfredsstiller kravet til forutberegnelighet. Det oppstÄr ogsÄ spÞrsmÄl om det er forsvarlig pÄ bakgrunn av kriminaliseringsprinsippene i strafferetten at ordlyden i § 3 kriminaliserer behandling av dyr som er i trÄd med allmenn akseptert praksis og normalt dyrehold. Dyrevelferdsloven stiller strengere krav til Ä anerkjenne dyrs rettigheter og status i samfunnet enn tidligere, og dette vil kunne fÄ betydning for hvordan dyrevelferdsloven § 3 skal tolkes
Hvordan forstÄr designere annerledeshet i arbeid med universell utforming? En kvalitativ studie av forstÄelse og suksesskriterier for utvikling av universell utforming i IKT
Denne masteroppgaven er en studie av hvordan interaksjonsdesignere og utviklere forstÄr annerledeshet og design for ulike menneskers behov. Arbeidet plasserer seg under forskning pÄ universell utforming i HCI-feltet, men stÞtter seg ogsÄ pÄ fagfeltet disability studies for Ä forstÄ annerledeshet og syn pÄ annerledeshet blant mennesker. I arbeidet har vi intervjuet designere og utviklere, blinde samt sterkt svaksynte brukere, og analysert gjeldende og vedtatt lovgivning. FormÄlet med oppgaven er Ä belyse problemer som oppstÄr i arbeid med utvikling av universell utforming av IKT-systemer. Vi diskuterer mulige Ärsaker og lÞsninger pÄ de problemene vi har identifisert i arbeidet, samt implikasjoner av ulike tiltak som kan innfÞres og av lovverk som er vedtatt Ä tre i kraft. Samtidig sammenligner vi vÄre funn med tidligere forskning pÄ suksessfulle prosjekter innenfor utvikling av universell utforming i IKT
Comparison of slipperiness on rubber mats and concrete floors for dairy cows using behavioural methods
A self-learning teacher-student framework for gastrointestinal image classification
Medical data is growing at an estimated 2.5 exabytes per year~\cite{GlobalInformation14}. However, medical data is often sparse and unavailable for the research community, and qualified medical personnel rarely have time for the tedious labeling work required to prepare the data. New screening methods of the gastrointestinal (GI) tract, like video capsule endoscopy (VCE), can help to reduce patients discomfort and help to increase screening capabilities. One of the main reasons why VCE is not more commonly used by medical experts is the amount of data it produces. A high level of extra work is required by the physicians who, depending on the patient, have to look at more than 50,000 frames per examination. To make VCE more accepted and useful data analysis methods such as machine learning can be very useful. Even if a lot of frames are collected per patient they are most of the time showing normal tissue without any relevant finding. This introduces another problem, namely that it is difficult to train a machine learning based method using this data. Existing models often struggle with the challenge of not having enough data that contains anomalies. This often leads to overfitted and not generalisable models. Our work explores ways to help existing models to overcome this problem by utilising a popular sub-category of machine learning called semi-supervised learning. Semi-supervised learning uses a combination of labeled and unlabeled data which allows us to take advantage of large amounts of unlabeled data. In this thesis, we introduce our proposed semi-supervised teacher-student framework. This framework is built specifically to take advantage of vast amount of unlabeled data and consists of three main steps: (1) train a teacher model with labeled data, (2) use the teacher model to infer pseudo labels with unlabeled data, and (3) train a new and larger student model with a combination of labeled images and inferred pseudo labels. These three steps are repeated several times by treating the student as a teacher to relabel the unlabeled data and consequently training a new student. We demonstrate that our framework can be of use for classifying both, VCE and endoscopic colonoscopy images or videos. We demonstrate that our teacher-student model can significantly increase the performance compared to traditional supervised-learning-based models. We believe that our framework has potential to be a useful addition to existing medical multimedia systems for automatic disease detection, because new data can be continuously added to improve the models performance while in production
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