Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image ...creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.
Towards quantifying the development value of code contributions Ren, Jinglei; Yin, Hezheng; Hu, Qingda ...
Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering,
10/2018
Conference Proceeding
Quantifying the value of developers’ code contributions to a software project requires more than simply counting lines of code or commits. We define the development value of code as a combination of ...its structural value (the effect of code reuse) and its non-structural value (the impact on development). We propose techniques to automatically calculate both components of development value and combine them using Learning to Rank. Our preliminary empirical study shows that our analysis yields richer results than those obtained by human assessment or simple counting methods and demonstrates the potential of our approach.
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