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A

Aasa Feragen

Technical University of Denmark

21H指数
155论文数
1.3K被引数
收录论文 17
发表时间
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Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound分类任务和分布偏移类型对胎儿超声图像中未知分布检测的影响
errMEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT VII
IF0
err2026-01-01
err0
PREAI
errWong, Chun Kit; Christensen, Anders N.; Bercea, Cosmin, I; Schnabel, Julia A.; Tolsgaard, Martin G.; Feragen, Aasa
err分享
err收藏
Diffusion-Based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment基于扩散的迭代式反事实解释用于胎儿超声图像质量评估
errSIMPLIFYING MEDICAL ULTRASOUND, ASMUS 2025
IF0
err2026-01-01
err0
PREAI
errPegios, Paraskevas; Lin, Manxi; Weng, Nina; Svendsen, Morten Bo Sondergaard; Bashir, Zahra; Bigdeli, Siavash; Christensen, Anders Nymark; Tolsgaard, Martin; Feragen, Aasa
err分享
err收藏
The Cervix in Context: Bias Assessment in Preterm Birth Prediction宫颈在背景下的作用：早产预测中的偏倚评估
errFAIRNESS OF AI IN MEDICAL IMAGING, FAIMI 2025
IF0
err2026-01-01
err0
PREAI
errFournel, Joris; Pegios, Paraskevas; Sejer, Emilie Pi Fogtmann; Tolsgaard, Martin; Feragen, Aasa
err分享
err收藏
Difficulty Estimation for Image-Specific Medical Image Segmentation Quality Control基于图像的医学图像分割质量控制的难度估计
errMEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT XIII
IF0
err2026-01-01
err0
PREAI
errFournel, Joris; Bartoli, Axel; Marchi, Baptiste; Maurin, Arnaud; Bigdeli, Siavash Arjomand; Jacquier, Alexis; Feragen, Aasa
err分享
err收藏
General Methods Make Great Domain-Specific Foundation Models: A Case-Study on Fetal Ultrasound通用方法构建出色的领域特定基础模型：胎儿超声案例研究
errMEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT VII
IF0
err2026-01-01
err0
PREAI
errAmbsdorf, Jakob; Munk, Asbjorn; Llambias, Sebastian; Christensen, Anders N.; Mikolaj, Kamil; Balestriero, Randall; Tolsgaard, Martin G.; Feragen, Aasa; Mads
err分享
err收藏
Fetal cardiac remodeling in second trimester in pregnancies with pre-eclampsia and/or fetal growth restriction: deep-learning-based approach using population-wide data妊娠中期的子痫前期和/或胎儿生长受限孕妇的胎儿心脏重塑：基于全人群数据深度学习的应用方法
errUltrasound in Obstetrics & Gynecology
IF6.3
err2025-10-30
err0
errOAAI
errC. A. Taksøe-Vester; K. Mikolaj; O. B. Petersen; C. K. Ekelund; N. G. Vejlstrup; C. Vedel; A. N. Christensen; A. Feragen; M. Nielsen; M. G. Tolsgaard
err分享
err收藏
The Combined Use of Cervical Ultrasound and Deep Learning Improves the Detection of Patients at Risk for Spontaneous Preterm Delivery联合应用颈项超声和深度学习可提高对自发早产风险患者的检出率
errAmerican Journal of Obstetrics and Gynecology
IF8.4
err2025-09-12
err0
PREAI
errEmilie P.F. Sejer; Paraskevas Pegios; Manxi Lin; Zahra Bashir; Camilla B. Wulff; Anders N. Christensen; Mads Nielsen; Aasa Feragen; Martin G. Tolsgaard
err分享
err收藏
Predicting abnormal fetal growth using deep learning使用深度学习预测异常胎儿生长
errNPJ DIGITAL MEDICINE
IF15.1
err2025-05-29
err0
errOAAI
errMikolaj, Kamil Wojciech; Christensen, Anders Nymark; Taksoe-Vester, Caroline Amalie; Feragen, Aasa; Petersen, Olav Bjorn; Lin, Manxi; Nielsen, Mads; Svendsen, Morten Bo Sondergaard; Tolsgaard, Martin Gronnebaek
err分享
err收藏
Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation
errSCIENTIFIC REPORTS
IF3.9
err2025-01-15
err0
errOAAI
errBashir, Zahra; Lin, Manxi; Feragen, Aasa; Mikolaj, Kamil; Taksoe-Vester, Caroline; Christensen, Anders Nymark; Svendsen, Morten B. S.; Fabricius, Mette Hvilshoj; Andreasen, Lisbeth; Nielsen, Mads; Tolsgaard, Martin Gronnebaek
err分享
err收藏
Society of Radiologists in Ultrasound response to Proposed simplified protocol for initial assessment of endometriosis with transvaginal ultrasound Reply
errULTRASOUND IN OBSTETRICS & GYNECOLOGY
IF6.3
err2024-12-15
err0
PREAI
errMikolaj, K.; Taksoe-Vester, C. A.; Petersen, O. B. B.; Vejlstrup, N. G.; Christensen, A. N.; Feragen, A.; Nielsen, M.; Svendsen, M. B. S.; Tolsgaard, M. G.
err分享
err收藏
Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta
errULTRASOUND IN OBSTETRICS & GYNECOLOGY
IF6.3
err2024-06-03
err7
errOAAI
errTaksoe-Vester, C. A.; Mikolaj, K.; Petersen, O. B. B.; Vejlstrup, N. G.; Christensen, A. N.; Feragen, A.; Nielsen, M.; Svendsen, M. B. S.; Tolsgaard, M. G.
err分享
err收藏
AI supported fetal echocardiography with quality assessment
errSCIENTIFIC REPORTS
IF3.9
err2024-03-09
err4
errOAAI
errTaksoee-Vester, Caroline A.; Mikolaj, Kamil; Bashir, Zahra; Christensen, Anders N.; Petersen, Olav B.; Sundberg, Karin; Feragen, Aasa; Svendsen, Morten B. S.; Nielsen, Mads; Tolsgaard, Martin G.
err分享
err收藏
The path toward equal performance in medical machine learning
errPATTERNS
IF7.4
err2023-07-01
err9
errOAAI
errPetersen, Eike; Holm, Sune; Ganz, Melanie; Feragen, Aasa
err分享
err收藏
Semantic similarity metrics for image registration
errMEDICAL IMAGE ANALYSIS
IF11.8
err2023-07-01
err3
errOAAI
errCzolbe, Steffen; Pegios, Paraskevas; Krause, Oswin; Feragen, Aasa
err分享
err收藏
Bias in context: What to do when complete bias removal is not an option
errPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
IF9.1
err2023-05-30
err2
errOAAI
errHolm, Sune; Petersen, Eike; Ganz, Melanie; Feragen, Aasa
err分享
err收藏
Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
errSCIENTIFIC REPORTS
IF3.9
err2023-02-08
err8
errOAAI
errAndreasen, Lisbeth Anita; Feragen, Aasa; Christensen, Anders Nymark; Thybo, Jonathan Kistrup; Svendsen, Morten Bo S.; Zepf, Kilian; Lekadir, Karim; Tolsgaard, Martin Gronnebaek
err分享
err收藏
Geodesic Atlas-Based Labeling of Anatomical Trees: Application and Evaluation on Airways Extracted From CT
errIEEE TRANSACTIONS ON MEDICAL IMAGING
IF9.8
err2015-06-01
err22
PREAI
errFeragen, Aasa; Petersen, Jens; Owen, Megan; Lo, Pechin; Thomsen, Laura Hohwu; Wille, Mathilde Marie Winkler; Dirksen, Asger; de Bruijne, Marleen
err分享
err收藏

研究方向

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合作学者
合作期刊
A
Asger Dirksen
H 指数: 61 · 论文数: 332
J
Julia A. Schnabel
H 指数: 51 · 论文数: 492
J
Jens Petersen
H 指数: 49 · 论文数: 263
M
Morten A. Nielsen
H 指数: 43 · 论文数: 124
M
Mads Nielsen
H 指数: 41 · 论文数: 375
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