Anzeige
Erschienen in:
2020 | OriginalPaper | Buchkapitel
U-Net in Constraint Few-Shot Settings
Enforcing Few-Sample-Fitting for Faster Convergence of U-Net for Femur Segmentation in X-Ray
verfasst von : Duc Duy Pham, Melanie Lausen, Gurbandurdy Dovletov, Sebastian Serong, Stefan Landgraeber, Marcus Jäger, Josef Pauli
Erschienen in: Bildverarbeitung für die Medizin 2020
Verlag: Springer Fachmedien Wiesbaden
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper, we investigate the feasibility of using a standard U-Net for Few-Shot segmentation tasks in very constraint settings. We demonstrate on the example of femur segmentation in X-ray images, that a U-Net architecture only needs few samples to generate accurate segmentations, if the images and the structure of interest only show little variance in appearance and perspective. This is often the case in medical imaging. We also present a novel training strategy for the UNet, leveraging U-Net’s Few-Shot capability for inter-patient consistent protocols. We propose repeatedly enforcing Few-Sample-Fitting the network for faster convergence. The results of our experiments indicate that incrementally fitting the network to an increasing sample set can lead to faster network convergence in constraint few-shot settings.