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Sabrina Kletz presented her work at the MIAR Workshop @ MICCAI 2019

Sabrina Kletz presented the paper “Learning the Representation of Instrument Images in Laparoscopy Video” at the MIAR Workshop @ MICCAI 2019 in Shenzhen, China.

Authors: Sabrina Kletz, Klaus Schoeffmann, Heinrich Husslein

Abstract: Automatic recognition of instruments in laparoscopy videos poses many challenges that need to be addressed like identifying multiple instruments appearing in various representations and in different lighting conditions which in turn may be occluded by other instruments, tissue, blood or smoke. Considering these challenges it may be beneficial for recognition approaches that instrument frames are first detected in a sequence of video frames for further investigating only these frames. This pre-recognition step is also relevant for many other classification tasks in laparoscopy videos such as action recognition or adverse event analysis. In this work, we address the task of binary classification to recognize video frames as either instrument or non-instrument images. We examine convolutional neural network models to learn the representation of instrument frames in videos and take a closer look at learned activation patterns. For this task, GoogLeNet together with batch normalization is trained and validated using a publicly available dataset for instrument count classifications. We compare transfer learning with learning from scratch and evaluate on datasets from cholecystectomy and gynecology. The evaluation shows that fine-tuning a pre-trained model on the instrument and non-instrument images is much faster and more stable in learning than training a model from scratch.

Conference: 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 13–17, 2018, Shenzhen, China

Track: Medical Imaging and Augmented Reality (MIAR) Workshop @MICCAI

Mathias Lux

Hüttenjam 2019 is over and it has been a hugely successful event

The Hüttenjam 2019 is over and it has been a hugely successful event. 26 participants jammed for two days and two nights in four chalets at Marktlalm, on Turracher Höhe. Six games have been developed matching the topic “Can’t see the wood for the trees”. Games ranged from multiplayer hide and seek, to simulations, platformers, stealth, and puzzle games. All games can be found on Itch.io [1]. Besides working on the games we could socialize and network with joint breakfast and lunches and trips to the Nockiflitzer and Panoramaalm. For the motivated participants, there were running sessions in the morning and a hike to Rinsennock. 

General feedback by the participants indicated that people wanted to have a second edition next year. Responses on social networks like Twitter, Facebook and Instagram indicated that many more people are interested beyond the ones already attending. For Twitter top tweets had more than 70 likes [2], with the initial Facebook video we reached more than 700 people within a month, with the latest video we reached more than 70 people in a day [3]. 

Thanks a lot to all the sponsors who made this possible: Bitmovin, Förderverein Technische Fakultät, and Technische Fakultät der Universität Klagenfurt. If you want to spread the word about the event, feel free to use any photos or video from [4].

[1] All games online: https://itch.io/jam/huettenjam

[2] Hüttenjam on Twitter https://twitter.com/search?q=%23h%C3%BCttenjam

[3] Gamebert on Facebook https://www.facebook.com/gamebert

[4] Photos and videos: https://photos.app.goo.gl/BNEPzjoLJwLiP4kR6

Christian Timmerer
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5GPlayground-Eröffnung mit ITEC-Use-Case “Virtual Realities”

Mit dem 5G Summit Carinthia, ein Kurzsymposium zur neuen Mobilfunktechnologie 5G, wurde heute der 5G Playground Carinthia feierlich eröffnet. Der 5G Playground Carinthia ist österreichweit die erste Serviceeinrichtung für die Erforschung und Weiterentwicklung von 5G-spezifischen Anwendungen, Services und Geschäftsmodellen. Das Bundesministerium für Verkehr, Innovation und Technology (BMVIT) sowie das Land Kärnten finanzieren dieses einzigartige Forschungslabor im Süden Österreichs. A1 Telekom Austria stellt die technische Infrastruktur zur Verfügung.

Der 5G Playground Carinthia bietet allen Forschungs-, Innovations- und Bildungseinrichtungen sowie KMUs und Start Ups die einzigartige Möglichkeit ihre Produkte und Anwendungen mit dieser neuen Technologie zu testen und im Echtbetrieb zu erproben.

Die Alpen-Adria-Universität Klagenfurt und insbesondere das Institut für Informationstechnologie beteiligt sich an dem 5GPlayground mit einen Use-Case über “Virtual Realities”. Das Projekt erforscht, entwickelt, erprobt und evaluiert ausgewählte VR-Anwendungen über 5G-Netze, z.B. Streaming von 360°-Videos und von neuen Formen immersiver Medien, etwa von volumetrischen Daten (Point Clouds). Diese Anwendungen erfordern und testen sowohl die hohen Datenraten als auch die extrem geringen Verzögerungszeiten von 5G-Netzen, im Downlink (Streaming zu einer VR-Brille) wie auch im Uplink (Streaming von Live-Inhalten von einer 360°-Kamera weg). Darüber hinaus werden Edge-Computing-Komponenten genutzt, die 5G vorsieht, um höhere Präsentationsqualität und raschere Reaktionszeiten des VR-Systems bei Bewegung/Interaktion eines Nutzers zu erreichen. Es werden VR-Systeme entwickelt, welche die Leistungsfähigkeit von 5G zu demonstrieren erlauben.

Link: https://5gplayground.at/

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Prof. Radu Prodan @ EU High level Social Media Symposium Panel Discussion

Read more about the High level Symposium.

Blockchain course in WS 2019 – Assoc.-Prof. Dr. Antorweep Chakravorty

BlockchainITEC is delighted to announce the next speaker in our guest lecture series – Dr. Antorweep Chakravorty from University of Stavanger and bitYoga, Norway. The course will take place from November 18 – 29, 2019

This course presents an introduction and further information about the high-interest topic blockchains. Please register at the course 623.714.

Further information is available HERE.

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3rd ARTICONF technical meeting in Porto.

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The first H2020 ASPIDE technical meeting took place in Klagenfurt

The Klagenfurt University hosted the first ASPIDE technical meeting (30th September – 2nd October), which aims on designing scalable software solutions for exascale computing.

2019 ASPIDE Meeting Klagenfurt

ASPIDE Meeting at Klagenfurt University

2019 ASPIDE Meeting Klagenfurt Social Event

2019 ASPIDE Meeting Klagenfurt (Social Event)

Natalia Sokolova
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MMM’20: Evaluating the Generalization Performance of Instrument Classification in Cataract Surgery Videos

Our paper has been accepted for publication at the MMM 2020 Conference on Multimedia Modeling. The work was conducted in the context of the ongoing OVID project.

Authors: Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer (AAU Klagenfurt); Doris
Putzgruber-Adamitsch, Yosuf El-Shabrawi (Klinikum Klagenfurt)

Abstract:
In the field of ophthalmic surgery, many clinicians nowadays record their microscopic procedures with a video camera and use the recorded footage for later purpose, such as forensics, teaching, or training. However, in order to efficiently use the video material after surgery, the video content needs to be analyzed automatically. Important semantic content to be analyzed and indexed in these short videos are operation instruments, since they provide an indication of the corresponding operation phase and surgical action. Related work has already shown that it is possible to accurately detect instruments in cataract surgery videos. However, their underlying dataset (from the CATARACTS challenge) has very good visual quality, which is not reflecting the typical quality of videos acquired in general hospitals. In this paper, we therefore analyze the generalization performance of deep learning models for instrument recognition in terms of dataset change. More precisely, we trained such models as ResNet-50, Inception v3 and NASNet Mobile using a dataset of high visual quality (CATARACT) and test it on another dataset with low visual quality (Cataract-101), and vice versa. Our results show that the generalizability is rather low in general, but clearly worse for the model trained on the high-quality dataset. Another important observation is the fact that the trained models are able to detect similar instruments in the other dataset even if their appearance is different.

The paper “GLENDA: Gynecologic Laparoscopy Endometriosis Dataset” has been accepted

The paper “GLENDA: Gynecologic Laparoscopy Endometriosis Dataset” has been accepted for publication at the Multimedia Datasets for Repeatable Experimentation (MDRE) special session, co-located at the 26th International Conference on Multimedia Modeling, MMM 2020 to be held at Daejon, Korea (January 5-8, 2020).

Authors: Andreas Leibetseder, Sabrina Kletz, Klaus Schoeffmann (Alpen-Adria Universität Klagenfurt), Simon Keckstein (Ludwig-Maximilians-University Munich), Jörg Keckstein (Ulm University)

Abstract: Gynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient’s abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) – an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.

Keywords: lesion detection, endometriosis localization, medical dataset, region-based annotations, gynecologic laparoscopy

Acknowledgement: This work was funded by the FWF Austrian Science Fund under grant P 32010-N38.

Philipp Moll
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Talk of Philipp Moll on the ACM ICN 2019 conference

Philipp Moll presented the paper “Inter-Server Game State Synchronization using Named Data Networking” on the ACM Conference on Information-Centric Networking 2019 in Macau, China.

Authors: Philipp Moll, Sebastian Theuermann, Natascha Rauscher, Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), Jeff Burke (UCLA)

Abstract: In this paper, we develop a system for inter-server game state synchronization using the NDN architecture. We use Minecraft as a real-world example of online games and extend Minecraft’s single-server architecture to work as multi-server game. In our prototype,we use two different NDN-based approaches for the dissemination of game state updates in server clusters. In a naive approach, servers request game state updates for small segments of the game worldfrom other servers of the cluster. In an improved approach – the region manifest approach– servers identify changed parts of the world by subscribing to manifest files containing information about world regions managed by the other servers of the cluster. An apparent downside of the NDN approaches is the high overhead when handling small-sized game state updates, but our evaluation shows that NDN already improves on IP-based implementations regarding the resulting traffic volume when three or more servers are involved. Furthermore, caused by NDN’s inherent multicast functionality, the advantage over IP increases with the size of theserver cluster. Moreover, the use of NDN-based approaches leads to benefits beyond traffic reduction only. The name-based host-independent access to world regions allows to scale server clusters easier.

The paper full paper can be found on: https://conferences.sigcomm.org/acm-icn/2019/proceedings/icn19-25.pdf