3 citations · 7 across the 7 of their papers we have counts for
9 papers
Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Ralf Raumanns, Theresa Elstner, Louis Ferger-Andrews +5
Machine learning courses often use pre-labelled datasets, hiding the subjectivity of human annotation. This produces an overly trusting view of data and AI models in students, at t…
Effect of Demographic Bias on Skin Lesion Classification
Ralf Raumanns, Gerard Schouten, Veronika Cheplygina +1
In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, parti…
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim +1
The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification u…
Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
Petra Heck, Gerard Schouten
For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machin…
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
Ralf Raumanns, Gerard Schouten, Max Joosten +2
We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visu…
Lessons Learned from Educating AI Engineers
Petra Heck, Gerard Schouten
Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this pr…
Turning Software Engineers into AI Engineers
Petra Heck, Gerard Schouten
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. With the educational programme…
Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels +2
One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algor…
Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
Ralf Raumanns, Elif K Contar, Gerard Schouten +1
Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outli…