Kavli Affiliate: Yi Zhou
| First 5 Authors: Yunqi Gu, Tao Zhou, Yizhe Zhang, Yi Zhou, Kelei He
| Summary:
Medical image segmentation plays a crucial role in computer-aided diagnosis.
However, existing methods heavily rely on fully supervised training, which
requires a large amount of labeled data with time-consuming pixel-wise
annotations. Moreover, accurately segmenting lesions poses challenges due to
variations in shape, size, and location. To address these issues, we propose a
novel Dual-scale Enhanced and Cross-generative consistency learning framework
for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a
Cross-level Feature Aggregation (CFA) module that integrates cross-level
adjacent layers to enhance the feature representation ability across different
resolutions. To address scale variation, we present a scale-enhanced
consistency constraint, which ensures consistency in the segmentation maps
generated from the same input image at different scales. This constraint helps
handle variations in lesion sizes and improves the robustness of the model.
Furthermore, we propose a cross-generative consistency scheme, in which the
original and perturbed images can be reconstructed using cross-segmentation
maps. This consistency constraint allows us to mine effective feature
representations and boost the segmentation performance. To further exploit the
scale information, we propose a Dual-scale Complementary Fusion (DCF) module
that integrates features from two scale-specific decoders operating at
different scales to help produce more accurate segmentation maps. Extensive
experimental results on multiple medical segmentation tasks (polyp, skin
lesion, and brain glioma) demonstrate the effectiveness of our DEC-Seg against
other state-of-the-art semi-supervised segmentation approaches. The
implementation code will be released at https://github.com/taozh2017/DECSeg.
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