Gastrointestinal 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.

Large-scale agile development has gained widespread interest in the software industry, but it is a topic with few empirical studies of practice. Development projects at scale introduce a range of new challenges in managing a large number of people and teams, often with high uncertainty about product requirements and technical solutions. The coordination of teams has been identified as one of the main challenges. This study presents a rich longitudinal explanatory case study of a very large software development programme with 10 development teams. We focus on inter-team coordination in two phases: one that applies a first-generation agile development method and another that uses a second-generation one. We identified 27 coordination mechanisms in the first phase, and 14 coordination mechanisms in the second. Based on an analysis of coordination strategies and mechanisms, we develop five propositions on how the transition from a first- to a second-generation method impacts coordination. These propositions have implications for theory and practice.