A Light in the Dark: Deep Learning Practices for Industrial Computer Vision - Harl, Maximilian; Herchenbach, Marvin; Kruschel, Sven; Hambauer, Nico; Zschech, Patrick; Kraus, Mathias (Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI), 2022)
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.<br
Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Transfer Learning; Quality Inspection

Coupling Neural Networks Between Clusters for Better Personalized Care - Kraus, Mathias; Hambauer, Nico; Müller, Kristina; Kröckel, Pavlina; Ulapane, Nalika; De Caigny, Arno; De Bock, Koen; Coussement, Kristof (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
Personalized healthcare powered by machine learning (ML) is at the forefront of modern medicine, promising to optimize treatment outcomes, reduce adverse effects, and improve patient satisfaction. However, simple ML models generally lack the complexity to accurately model individual characteristics, while powerful ML models require large amounts of data, which are often unavailable in the healthcare domain. We address this problem with cluster-level personalization. In this method, similar patients are grouped into clusters and a local ML model is trained for each cluster. Since the amount of patient data to train ML models naturally decreases for each cluster, we introduce a novel objective function called "coupling" that allows information to be shared between clusters, so that smaller clusters can also benefit from information from larger clusters, thereby improving patient outcome prediction. Our method provides a compromise between a single global model for all patients and completely independent local cluster models. We show that coupling leads to statistically significant improvements on a simulated and a real-world dataset in the context of diabetes.<br
Keywords: Personal Health Management with Digital Solutions; machine learning; neural networks; optimization; personalized care

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints - Zschech, Patrick; Weinzierl, Sven; Hambauer, Nico; Zilker, Sandra; Kraus, Mathias (Proceedings of the 30th European Conference on Information Systems, 2022)
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.<br
Keywords: Predictive Analytics; Interpretable Machine Learning; Generalized Additive Models; Chancengleichheit