Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Wirtualizacja pracy w globalnych łańcuchach dostaw

Background: The paper is devoted to the notion and benefits of implementing virtual work in global supply chains. Virtual work must be understood as an intentional activity of a human being, aimed at rendering services (tangible and intangible), by means of ITC tools, performed in a distance from the traditional place of work, in a mobile manner. The empirical research were conducted on the basis of 4 case studies of global leaders of supply chains, which in accordance with M. Fisher's classification, represent two types. The case studies confirmed the positive influence of virtual work both in effective and flexible supply chains. Favourable market and technological conditions and increasing awareness of benefits of virtual work will make it more and more widespread in companies comprising global supply chains. The aim of the study is to demonstrate the cause and effect relationships between virtual work and competitiveness of efficient and flexible supply chain. Methods: The paper is based on the available recent scientific-theoretical research and publication. The authors analyzed 4 enterprises in Poland. The enterprises representing a flexible or an effective supply chain, either using or not a virtual work. The study carried out the authors had the form of individual interviews. The authors used case studies to show that virtual work brings notable benefits in an effective and flexible supply chain. Results: Based on these case studies, the authors demonstrated reasons to implement virtual work in selected enterprises. The reasons to implement virtual work are determinants of possible achieve economies in effective and flexible supply chain. Conclusions: The examined case studies show that virtual work brings different benefits. In the effective supply chain, virtual workers enable to increase effectiveness and financial results for example. In the flexible supply chain the virtual work can be a way to maintain and build long-term relations with suppliers and customers.Wstęp: Artykuł poświęcony jest pojęciu oraz korzyściom z wdrożenia wirtualnej pracy w globalnych łańcuchach dostaw. Wirtualną pracę należy rozumieć jako celowe działanie człowieka, której celem jest świadczenie usług (materialnych i niematerialnych), za pomocą narzędzi teleinformatycznych, wykonywanych zdalnie od tradycyjnego miejsca pracy, w sposób mobilny. Badania empiryczne zostały przygotowane na podstawie 4 studiów przypadku globalnych liderów łańcuchów dostaw, które zgodnie z klasyfikacją M. Fishera reprezentują dwa ich typy. Studia przypadków potwierdziły pozytywny wpływ wirtualnej pracy zarówno w efektywnych i elastycznych łańcuchach dostaw. Korzystne warunki rynkowe i technologiczne oraz zwiększenie świadomości korzyści płynących z wirtualnej pracy będzie wpływało na powszechniejsze jej stosowanie w firmach tworzących globalne łańcuchy dostaw. Metody: Praca została przygotowana w oparciu o dostępne badania zarówno teoretyczne jak i praktyczne. Autorzy przeanalizowali 4 przedsiębiorstwa w Polsce. Przedsiębiorstwa reprezentują elastyczny i efektywny łańcuch dostaw, wykorzystujące lub nie, wirtualną pracę. Badanie przeprowadzone przez autorów miały formę wywiadów indywidualnych. Autorzy wykorzystali studia przypadków w celu pokazania, że wirtualna praca przynosi wymierne korzyści w efektywnym i elastycznym łańcuch dostaw. Rezultaty: Na podstawie studiów przypadków, autorzy wykazali przesłanki wdrożenia wirtualnej pracy w wybranych przedsiębiorstwach. Przesłanki wdrożenia wirtualnej pracy są wyznacznikami możliwości uzyskania korzyści w efektywnym i elastycznym łańcuch dostaw. Wnioski: Badane studia przypadków pokazują, że wirtualna praca przynosi odmienne korzyści. W efektywnym łańcuchu dostaw, pracownicy wirtualni dla przykładu umożliwiają zwiększenie skuteczności jego działania i wyniki finansowe. W elastycznym łańcuchu dostaw praca wirtualna może być sposobem na utrzymywanie i budowanie długoterminowych relacji z dostawcami i klientami

Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area