Zobrazeno 1 - 10
of 1 809
pro vyhledávání: '"Aasa, A."'
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 providing actionable explanations to practitioners. Previously, counte
Externí odkaz:
http://arxiv.org/abs/2411.02259
Autor:
Olsen, Markus Ditlev Sjøgren, Ambsdorf, Jakob, Lin, Manxi, Taksøe-Vester, Caroline, Svendsen, Morten Bo Søndergaard, Christensen, Anders Nymark, Nielsen, Mads, Tolsgaard, Martin Grønnebæk, Feragen, Aasa, Pegios, Paraskevas
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 on supervised learning, rely on manual annotations, and risk missing
Externí odkaz:
http://arxiv.org/abs/2408.03654
We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal annotator variation simple deterministic models can suffice, and lung lesi
Externí odkaz:
http://arxiv.org/abs/2407.16367
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups -
Externí odkaz:
http://arxiv.org/abs/2406.12142
Autor:
Zhang, Tiantian, Lin, Manxi, Guo, Hongda, Zhang, Xiaofan, Chiu, Ka Fung Peter, Feragen, Aasa, Dou, Qi
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-RADS scoring methods often lack the incorporation of common PI-RADS cli
Externí odkaz:
http://arxiv.org/abs/2405.08786
Autor:
Wong, Chun Kit, Ngo, Mary, Lin, Manxi, Bashir, Zahra, Heen, Amihai, Svendsen, Morten Bo Søndergaard, Tolsgaard, Martin Grønnebæk, Christensen, Anders Nymark, Feragen, Aasa
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 framework designed for deploying image-based AI models in such settings. U
Externí odkaz:
http://arxiv.org/abs/2404.00032
Autor:
Pegios, Paraskevas, Lin, Manxi, Weng, Nina, Svendsen, Morten Bo Søndergaard, Bashir, Zahra, Bigdeli, Siavash, Christensen, Anders Nymark, Tolsgaard, Martin, Feragen, Aasa
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, producing high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or the fetu
Externí odkaz:
http://arxiv.org/abs/2403.08700
Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria
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 perpetuating and amplifying harmful biases in applications. Gender st
Externí odkaz:
http://arxiv.org/abs/2403.08564
Autor:
Lin, Manxi, Weng, Nina, Mikolaj, Kamil, Bashir, Zahra, Svendsen, Morten Bo Søndergaard, Tolsgaard, Martin, Christensen, Anders Nymark, Feragen, Aasa
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 set. While existing research primarily investigates this in the realm of
Externí odkaz:
http://arxiv.org/abs/2403.06748
Autor:
Lin, Manxi, Ambsdorf, Jakob, Sejer, Emilie Pi Fogtmann, Bashir, Zahra, Wong, Chun Kit, Pegios, Paraskevas, Raheli, Alberto, Svendsen, Morten Bo Søndergaard, Nielsen, Mads, Tolsgaard, Martin Grønnebæk, Christensen, Anders Nymark, Feragen, Aasa
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 and annotations are noisy, we design a robust coarse-to-fine model that
Externí odkaz:
http://arxiv.org/abs/2402.08294