The MCMI research group headed by Prof. Dr. Christin Seifert focuses on the transfer of fundamental research in machine learning, information extraction, natural language processing and semantic technologies to applications in the medical domain, and to oncology in particular.
We address questions relevant, but not limited to understanding and transforming clinical documents, sensor data processing, predictive modelling, medical decision support, explaining decisions of complex machine learning models, and devising interpretable, yet accurate models to foster stakeholder acceptance and trust.
The MCMI research group is part of the Cancer Research Center Cologne Essen (CCCE), the Initiative Network of Excellence in Cancer Medicine NRW.
Girardethaus Girardetstr. 2 House 2, 2nd floor 45131 Essen
N.N. Office
@article{Nauta2023_csur_evaluating-xai-survey, author = {Nauta, Meike and Trienes, Jan and Pathak, Shreyasi and Nguyen, Elisa and Peters, Michelle and Schmitt, Yasmin and Schl\"{o}tterer, J\"{o}rg and van Keulen, Maurice and Seifert, Christin}, journal = {ACM Comput. Surv.}, title = {From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI}, year = {2023}, issn = {0360-0300}, month = feb, address = {New York, NY, USA}, doi = {10.1145/3583558}, keywords = {explainability, explainable AI, explainable artificial intelligence, XAI, interpretable machine learning, interpretability, quantitative evaluation methods, evaluation}, publisher = {Association for Computing Machinery}, url = {https://doi.org/10.1145/3583558} }
@article{Rietberg2023_mdpi_classifying-ms-patient-reports, author = {Rietberg, Max Tigo and Nguyen, Van Bach and Geerdink, Jeroen and Vijlbrief, Onno and Seifert, Christin}, journal = {Diagnostics}, title = {Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models}, year = {2023}, issn = {2075-4418}, number = {7}, volume = {13}, article-number = {1251}, doi = {10.3390/diagnostics13071251}, url = {https://www.mdpi.com/2075-4418/13/7/1251} }
@article{Borys2023_ejr_xai-in-medical-saliency, author = {Borys, Katarzyna and {Alyssa Schmitt}, Yasmin and Nauta, Meike and Seifert, Christin and Krämer, Nicole and Friedrich, Christoph M. and Nensa, Felix}, journal = {European Journal of Radiology}, title = {Explainable AI in Medical Imaging: An overview for clinical practitioners – Saliency-based XAI approaches}, year = {2023}, issn = {0720-048X}, pages = {110787}, doi = {https://doi.org/10.1016/j.ejrad.2023.110787}, keywords = {Explainable AI, Medical Imaging, Radiology, Black-Box, Explainability, Interpretability}, url = {https://www.sciencedirect.com/science/article/pii/S0720048X23001018} }
@article{Borys2023_ejr_xai-in-medical-beyond-saliency, author = {Borys, Katarzyna and Schmitt, Yasmin Alyssa and Nauta, Meike and Seifert, Christin and Krämer, Nicole and Friedrich, Christoph M. and Nensa, Felix}, journal = {European Journal of Radiology}, title = {Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches}, year = {2023}, issn = {0720-048X}, pages = {110786}, volume = {162}, doi = {https://doi.org/10.1016/j.ejrad.2023.110786}, keywords = {Explainable AI, Medical imaging, Radiology, Black-Box, Explainability, Interpretability}, url = {https://www.sciencedirect.com/science/article/pii/S0720048X23001006} }
@article{Lu2023_tsnre_channel-contribution-sleep-scoring, author = {Lu, Changqing and Pathak, Shreyasi and Englebienne, Gwenn and Seifert, Christin}, journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering}, title = {Channel Contribution In Deep Learning Based Automatic Sleep Scoring – How Many Channels Do We Need?}, year = {2023}, pages = {494-505}, doi = {10.1109/TNSRE.2022.3227040} }
jan.trienes@uni-due.de, jan.trienes@uk-essen.de Interests: Natural Language Processing and Information Retrieval
