3 citations · 8 across the 19 of their papers we have counts for
6 papers · 1 filter
Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
Aditya Parikh, Sneha Das, Aasa Feragen
Algorithmic bias in medical imaging can perpetuate health disparities, yet its causes remain poorly understood in segmentation tasks. While fairness has been extensively studied in…
Who Does Your Algorithm Fail? Investigating Age and Ethnic Bias in the MAMA-MIA Dataset
Aditya Parikh, Sneha Das, Aasa Feragen
Deep learning models aim to improve diagnostic workflows, but fairness evaluation remains underexplored beyond classification, e.g., in image segmentation. Unaddressed segmentation…
The Cost-Benefit of Interdisciplinarity in AI for Mental Health
Katerina Drakos, Eva Paraschou, Simay Toplu +4
Artificial intelligence has been introduced as a way to improve access to mental health support. However, most AI mental health chatbots rely on a limited range of disciplinary inp…
Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
Harald Vilhelm Skat-Rørdam, Sneha Das, Kathrine Sofie Rasmussen +2
Accurate evaluation of event detection in time series is essential for applications such as stress monitoring with wearable devices, where ground truth is typically annotated as si…
EmoTale: An Enacted Speech-emotion Dataset in Danish
Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen +1
While multiple emotional speech corpora exist for commonly spoken languages, there is a lack of functional datasets for smaller (spoken) languages, such as Danish. To our knowledge…
Exploring Local Interpretable Model-Agnostic Explanations for Speech Emotion Recognition with Distribution-Shift
Maja J. Hjuler, Line H. Clemmensen, Sneha Das
We introduce EmoLIME, a version of local interpretable model-agnostic explanations (LIME) for black-box Speech Emotion Recognition (SER) models. To the best of our knowledge, this…