% Keywords: Artificial Intelligence
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@Article{Jha2021,
  author = {Debesh Jha and Sharib Ali and Steven Hicks and Vajira Thambawita and Hanna Borgli and Pia H. Smedsrud and Thomas de Lange and Konstantin Pogorelov and Xiaowei Wang and Philipp Harzig and Minh-Triet Tran and Wenhua Meng and Trung-Hieu Hoang and Danielle Dias and Tobey H. Ko and Taruna Agrawal and Olga Ostroukhova and Zeshan Khan and Muhammad Atif Tahir and Yang Liu and Yuan Chang and Mathias Kirkerod and Dag Johansen and Mathias Lux and Haavard D. Johansen and Michael A. Riegler and Paal Halvorsen},
  journal = {Medical Image Analysis},
  title = {{A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging}},
  year = {2021},
  issn = {1361-8415},
  month = {may},
  pages = {102007},
  volume = {70},
  abstract = {Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.},
  doi = {10.1016/j.media.2021.102007},
  keywords = {Gastrointestinal endoscopy challenges, Artificial intelligence, Computer-aided detection and diagnosis, Medical imaging, Medico Task 2017, Medico Task 2018, BioMedia 2019 grand challenge},
  publisher = {Elsevier (BV)},
  url = {https://www.sciencedirect.com/science/article/pii/S1361841521000530?via=ihub}
}
@Article{Saurabh2020,
  author = {Nishant Saurabh and Shajulin Benedict and Jorge G. Barbosa and Radu Prodan},
  journal = {Journal of Parallel and Distributed Computing},
  title = {{Expelliarmus: Semantic-centric virtual machine image management in IaaS Clouds}},
  year = {2020},
  issn = {0743-7315},
  month = {dec},
  pages = {107--121},
  volume = {146},
  abstract = {Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3 - 22  times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.},
  doi = {10.1016/j.jpdc.2020.08.001},
  keywords = {Theoretical Computer Science, Computer Networks and Communications, Hardware and Architecture, Software, Artificial Intelligence},
  publisher = {Elsevier BV},
  url = {https://www.sciencedirect.com/science/article/pii/S0743731520303415}
}