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Found 4 papers, 2 papers with code
MDF-Net for abnormality detection by fusing X-rays with clinical data
In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data).
Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study
This paper proposes a novel multimodal DL architecture incorporating medical images and eye-tracking data for abnormality detection in chest x-rays.
Benchmarking Counterfactual Algorithms for XAI: From White Box to Black Box
This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model).
DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach
We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability.
