4 citations · 9 across the 3 of their papers we have counts for
3 papers
Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
Jingru Fu, Simone Bendazzoli, Örjan Smedby +1
Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when applied t…
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
Mehdi Astaraki, Francesca De Benetti, Yousef Yeganeh +5
Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering…
A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
Irene Brusini, Daniel Ferreira Padilla, José Barroso +4
Brain MRI segmentation results should always undergo a quality control (QC) process, since automatic segmentation tools can be prone to errors. In this work, we propose two deep le…