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Found 6 papers, 2 papers with code
Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
A production-ready AI system needs to be trustworthy, i. e. of high quality.
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources.
Lessons Learned from Educating AI Engineers
The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer.
Turning Software Engineers into AI Engineers
In industry as well as education as well as academics we see a growing need for knowledge on how to apply machine learning in software applications.
Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Surprisingly, we found that papers focusing on diagnosis rarely describe the demographics of the datasets used, and the diagnosis is purely based on images.
Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
Machine learning has a recognised need for large amounts of annotated data.
