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Found 4 papers, 3 papers with code
Feature importance to explain multimodal prediction models. A clinical use case
In this work, we develop a multimodal deep-learning model for post-operative mortality prediction using pre-operative and per-operative data from elderly hip fracture patients.
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
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.
Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings
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.
Interpreting and Correcting Medical Image Classification with PIP-Net
We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.
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