8 citations · 12 across the 20 of their papers we have counts for
21 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…
Effect of Demographic Bias on Skin Lesion Classification
Ralf Raumanns, Gerard Schouten, Veronika Cheplygina +1
In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, parti…
Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
Aditya Parikh, Stella Frank, Sneha Das +1
Biases in annotation pipelines may introduce label bias: when annotation quality or style varies systematically across demographic subgroups, causing disparities in performance and…
Intersectional Disentangling of Temporal and Acquisition Bias in Fetal Ultrasound
Aya Elgebaly, Joris Fournel, Benjamin Laine Jønch Jurgensen +5
Fairness studies of medical imaging AI often explain subgroup performance gaps through under-representation in the training data. We show that intersectional analysis can disentang…
Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning
Aditya Parikh, Aasa Feragen
We present a fairness-aware framework for multi-class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge at the PHAROS-AIF-MIH Worksho…
Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation
Aditya Parikh, Aasa Feragen, Sneha Das +1
Reliable deployment of Vision-Language Models (VLMs) in radiology requires validation metrics that go beyond surface-level text similarity to ensure clinical fidelity and demograph…
Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models
Chun Kit Wong, Paraskevas Pegios, Nina Weng +4
Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The c…
Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound
Chun Kit Wong, Anders N. Christensen, Cosmin I. Bercea +3
Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical se…
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
Jakob Ambsdorf, Asbjørn Munk, Sebastian Llambias +6
With access to large-scale, unlabeled medical datasets, researchers are confronted with two questions: Should they attempt to pretrain a custom foundation model on this medical dat…
Patronus: Interpretable Diffusion Models with Prototypes
Nina Weng, Aasa Feragen, Siavash Bigdeli
Uncovering the opacity of diffusion-based generative models is urgently needed, as their applications continue to expand while their underlying procedures largely remain a black bo…
Graph Counterfactual Explainable AI via Latent Space Traversal
Andreas Abildtrup Hansen, Paraskevas Pegios, Anna Calissano +1
Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these mo…
Counterfactual Explanations via Riemannian Latent Space Traversal
Paraskevas Pegios, Aasa Feragen, Andreas Abildtrup Hansen +1
The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions. Counterfactual explanations form a powerful tool for…
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Markus Ditlev Sjøgren Olsen, Jakob Ambsdorf, Manxi Lin +7
Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based…
Navigating Uncertainty in Medical Image Segmentation
Kilian Zepf, Jes Frellsen, Aasa Feragen
We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal anno…
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Vincent Olesen, Nina Weng, Aasa Feragen +1
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical…
Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS Scoring
Tiantian Zhang, Manxi Lin, Hongda Guo +4
The Prostate Imaging Reporting and Data System (PI-RADS) is pivotal in the diagnosis of clinically significant prostate cancer through MRI imaging. Current deep learning-based PI-R…
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Chun Kit Wong, Mary Ngo, Manxi Lin +6
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic fram…
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Paraskevas Pegios, Manxi Lin, Nina Weng +6
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the…
Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria
Sara Sterlie, Nina Weng, Aasa Feragen
Generative AI, such as large language models, has undergone rapid development within recent years. As these models become increasingly available to the public, concerns arise about…
Shortcut Learning in Medical Image Segmentation
Manxi Lin, Nina Weng, Kamil Mikolaj +5
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training se…
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer +9
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging an…