Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical
Image Segmentation
- URL: http://arxiv.org/abs/2109.09960v1
- Date: Tue, 21 Sep 2021 04:47:42 GMT
- Title: Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical
Image Segmentation
- Authors: Yicheng Wu, Zongyuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong
Xia and Jianfei Cai
- Abstract summary: We propose a novel mutual consistency network (MC-Net+) to exploit the unlabeled hard regions for semi-supervised medical image segmentation.
The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions.
We compare the segmentation results of the MC-Net+ with five state-of-the-art semi-supervised approaches on three public medical datasets.
- Score: 68.9233942579956
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this paper, we proposed a novel mutual consistency network (MC-Net+) to
effectively exploit the unlabeled hard regions for semi-supervised medical
image segmentation. The MC-Net+ model is motivated by the observation that deep
models trained with limited annotations are prone to output highly uncertain
and easily mis-classified predictions in the ambiguous regions (e.g. adhesive
edges or thin branches) for the image segmentation task. Leveraging these
region-level challenging samples can make the semi-supervised segmentation
model training more effective. Therefore, our proposed MC-Net+ model consists
of two new designs. First, the model contains one shared encoder and multiple
sightly different decoders (i.e. using different up-sampling strategies). The
statistical discrepancy of multiple decoders' outputs is computed to denote the
model's uncertainty, which indicates the unlabeled hard regions. Second, a new
mutual consistency constraint is enforced between one decoder's probability
output and other decoders' soft pseudo labels. In this way, we minimize the
model's uncertainty during training and force the model to generate invariant
and low-entropy results in such challenging areas of unlabeled data, in order
to learn a generalized feature representation. We compared the segmentation
results of the MC-Net+ with five state-of-the-art semi-supervised approaches on
three public medical datasets. Extension experiments with two common
semi-supervised settings demonstrate the superior performance of our model over
other existing methods, which sets a new state of the art for semi-supervised
medical image segmentation.
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