14 citations · 15 across the 5 of their papers we have counts for
6 papers
CRANE: Post-Hoc Counterfactual Retrieval for Multimodal Classifiers
Franco Rugolon, Ioanna Miliou, Panagiotis Papapetrou
Counterfactual explanation methods have been introduced for unimodal settings, with dedicated frameworks for tabular, timeseries, text, and image data, yet no post-hoc method has b…
Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort
Franco Rugolon, Korbinian Randl, Braslav Jovanovic +2
Multimodal Machine Learning offers a holistic view of a patient's status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework…
CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading
Pranav Raikote, Korbinian Randl, Ioanna Miliou +2
Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend t…
Counterfactual Explanations for Time Series Forecasting
Zhendong Wang, Ioanna Miliou, Isak Samsten +1
Among recent developments in time series forecasting methods, deep forecasting models have gained popularity as they can utilize hidden feature patterns in time series to improve f…
Early prediction of the risk of ICU mortality with Deep Federated Learning
Korbinian Randl, Núria Lladós Armengol, Lena Mondrejevski +1
Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and…
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino +3
Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnose…