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Found 9 papers, 5 papers with code
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.
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
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.
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.
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification
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.
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
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)
Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques
However, the performance of predicting in- and outflux is less sensitive to the prediction horizon.
