Uncertainty-aware Mean Teacher Framework with Inception and Squeeze-and-Excitation Block for MICCAI FLARE22 Challenge

Hui Meng

Keywords: semi-supervised learning, multi-organ segmentation, uncertainty estimation, multi-scale features

Abstract:

Semi-supervised learning has attracted extensive attention
in the field of medical image analysis. However, as a fundamental task,
semi-supervised segmentation has not been investigated sufficiently in
the field of multi-organ segmentation from abdominal CT. Therefore,
we propose a novel uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block (UMT-ISE). Specifically, the
UMT-ISE consists of a teacher model and a student model, in which the
student model learns from the teacher model by minimizing segmentation
loss and consistency loss. Additionaly, we adopt an uncertainty-aware algorithm to make the student model learn accurate and reliable targets
by making full use of uncertainty information. To capture multi-scale
features, the inception and squeeze-and-excitation block are incoporated
into the UMT-ISE. It is worth noting that abdominal CT of test cases are
first extracted before multi-organ segmentation in the inference phase,
which significantly improves segmentation accuracy.