Improving Glioma Segmentation in Low-Resolution Domains with Transfer Learning

Juampablo E Heras Rivera, Harshitha Rebala, Tianyi Ren, Abhishek Sharma, Mehmet Kurt

Keywords: Tumor segmentation, transfer learning, domain adaptation, low-resolution tumor segmentation

Abstract:

Training accurate tumor segmentation models only using data from the BraTS Sub-Saharan
Africa (SSA) Glioma dataset is difficult due to the low quantity and resolution of the images.
However, it is possible to improve model performance through the use of transfer learning
methods which leverage insights gained from larger datasets, such as the BraTS23 Adult
Glioma dataset. Here, we evaluate the performance of various transfer learning approaches
on the task of improving tumor segmentation Dice and Hausdorff Distance (95%) scores on
the BraTS SSA dataset. The transfer learning approaches assessed here include: Domain
Adversarial Neural Networks, Fine Tuning (with and without freezing layer weights), and
training with a combined dataset of low- and high-resolution images.

Submission Number: 168