Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound

no code implementations • 22 Mar 2024 • Chun Kit Wong, Mary Ngo, Manxi Lin, Zahra Bashir, Amihai Heen, Morten Bo Søndergaard Svendsen, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited.

Learning semantic image quality for fetal ultrasound from noisy ranking annotation

no code implementations • 13 Feb 2024 • Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer, Zahra Bashir, Chun Kit Wong, Paraskevas Pegios, Alberto Raheli, Morten Bo Søndergaard Svendsen, Mads Nielsen, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen

We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements.

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

no code implementations • 11 Apr 2023 • Chun Kit Wong, Manxi Lin, Alberto Raheli, Zahra Bashir, Morten Bo Søndergaard Svendsen, Martin Grønnebæk Tolsgaard, Aasa Feragen, Anders Nymark Christensen

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health.

Training CNN Classifiers for Semantic Segmentation using Partially Annotated Images: with Application on Human Thigh and Calf MRI

1 code implementation • 16 Aug 2020 • Chun Kit Wong, Stephanie Marchesseau, Maria Kalimeri, Tiang Siew Yap, Serena S. H. Teo, Lingaraj Krishna, Alfredo Franco-Obregón, Stacey K. H. Tay, Chin Meng Khoo, Philip T. H. Lee, Melvin K. S. Leow, John J. Totman, Mary C. Stephenson

Results: Evaluation against publicly available CT datasets shows that presence masking is a viable method for training class-generic classifiers.

Semantic Segmentation