Luca Zigiotto

Supervised white matter bundle segmentation in glioma patients with transfer learning

This paper evaluates transfer learning for adapting a deep learning model trained on healthy data to segment white matter bundles in glioma patients, finding performance drops due to domain shift and partial success of transfer learning.

CAN TRANSFER LEARNING IMPROVE SUPERVISED SEGMENTATIONOF WHITE MATTER BUNDLES IN GLIOMA PATIENTS?

This study investigates whether transfer learning can improve segmentation of white matter bundles in glioma patients by addressing domain shift between healthy and patient data.

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Classifyber improves white matter bundle segmentation by combining atlas, connectivity, and geometry data into a linear model, showing robustness across diverse datasets.