Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains

Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplines such as histopathology, this is crucial since large datasets with detailed annotations are scarce. While the majority...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2023-02
Hauptverfasser:	Thandiackal, Kevin, Piccinelli, Luigi, Pati, Pushpak, Goksel, Orcun
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Adaptation Alignment Annotations Artificial neural networks Datasets Domains Image resolution Image segmentation Machine learning Medical imaging
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!