31 citations · 31 across the 4 of their papers we have counts for
4 papers
Deep learning-based auto-segmentation of paraganglioma for growth monitoring
E. M. C. Sijben, J. C. Jansen, M. de Ridder +2
Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for mon…
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals
E. M. C. Sijben, J. C. Jansen, P. A. N. Bosman +1
Paragangliomas are rare, primarily slow-growing tumors for which the underlying growth pattern is unknown. Therefore, determining the best care for a patient is hard. Currently, if…
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models
E. M. C. Sijben, T. Alderliesten, P. A. N. Bosman
Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insi…
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur +1
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a m…