Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges

Autor: Pathak, Shreyasi, Schlötterer, Jörg, Veltman, Jeroen, Geerdink, Jeroen, van Keulen, Maurice, Seifert, Christin

Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainable models, like prototype-based models, can be especially beneficial as

Externí odkaz: http://arxiv.org/abs/2403.20260

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Autor: Nauta, Meike, Trienes, Jan, Pathak, Shreyasi, Nguyen, Elisa, Peters, Michelle, Schmitt, Yasmin, Schlötterer, Jörg, van Keulen, Maurice, Seifert, Christin

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often pres

Externí odkaz: http://arxiv.org/abs/2201.08164

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Multimodal Machine Learning for 30-Days Post-Operative Mortality Prediction of Elderly Hip Fracture Patients

Autor: Yenidogan, Berk, Pathak, Shreyasi, Geerdink, Jeroen, Hegeman, Johannes H., Van Keulen, Maurice, Xue, Bing, Pechenizkiy, Mykola, Koh, Yun Sing

Publikováno v: Proceedings-21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, 508-516
STARTPAGE=508;ENDPAGE=516;TITLE=Proceedings-21st IEEE International Conference on Data Mining Workshops, ICDMW 2021

Hip fractures in the elderly are a major health care problem in the society. In the clinic, it is important for medical specialists to identify high-risk patients to assist them in the decision-making process in choosing the right treatment. In this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ebaa61e53e7812ca6e2a9a0df47c739
https://doi.org/10.1109/icdmw53433.2021.00068

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