1 code implementation • 29 Mar 2024 • Shreyasi Pathak, Jörg Schlötterer, Jeroen Veltman, Jeroen Geerdink, Maurice van Keulen, Christin Seifert
Specifically, we apply three state-of-the-art prototype-based models, ProtoPNet, BRAIxProtoPNet++ and PIP-Net on mammography images for breast cancer prediction and evaluate these models w. r. t.
1 code implementation • 7 Dec 2023 • Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu
E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.
1 code implementation • 19 Oct 2023 • Shreyasi Pathak, Jörg Schlötterer, Jeroen Geerdink, Onno Dirk Vijlbrief, Maurice van Keulen, Christin Seifert
We show that two-level MIL can be applied in realistic clinical settings where only case labels, and a variable number of images per patient are available.
1 code implementation • 19 Jul 2023 • Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.
1 code implementation • CVPR 2023 • Meike Nauta, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
Driven by the principle of explainability-by-design, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision.
no code implementations • 20 Jan 2022 • Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 29 Nov 2019 • Jesper Provoost, Luc Wismans, Sander Van der Drift, Andreas Kamilaris, Maurice van Keulen
However, the performance of predicting in- and outflux is less sensitive to the prediction horizon.