6 papers
False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation
Linus Juni, Aasa Feragen, Aditya Parikh
Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tas…
Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
Aditya Parikh, Stella Frank, Sneha Das +1
Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias: group-conditional label errors that cause systematic performance disparitie…
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…
Exploring Facial Biomarkers for Detecting Depression through Temporal Analysis of Action Units
Aditya Parikh, Misha Sadeghi, Robert Richer +9
Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic metho…
Information Extraction from Unstructured data using Augmented-AI and Computer Vision
Aditya Parikh
Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conve…