Calibration effects of pseudo-label noise in semi-supervised training

Determine how error-correlated noise introduced by pseudo-labels in semi-supervised training affects calibration for brain-tumour segmentation models trained under the BraTS-GoAT data constraints.

Background

The paper identifies semi-supervised training as a possible extension but notes that the BraTS-GoAT challenge permits no external data. Although pseudo-labels could enable semi-supervised learning using available data, they may introduce noise correlated with model errors. The authors explicitly leave unresolved how this noise would affect the calibration of the resulting segmentation models.