RTNet: Relation Transformer Network for Diabetic Retinopathy
Multi-lesion Segmentation
- URL: http://arxiv.org/abs/2201.11037v1
- Date: Wed, 26 Jan 2022 16:19:04 GMT
- Title: RTNet: Relation Transformer Network for Diabetic Retinopathy
Multi-lesion Segmentation
- Authors: Shiqi Huang, Jianan Li, Yuze Xiao, Ning Shen and Tingfa Xu
- Abstract summary: We find that certain lesions are closed to specific vessels and present relative patterns to each other.
A self-attention transformer exploits global dependencies among lesion features, while a cross-attention transformer allows interactions between lesion and vessel features.
By integrating the above blocks of dual-branches, our network segments the four kinds of lesions simultaneously.
- Score: 10.643730843316948
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of
assisting ophthalmologists in diagnosis. Although many researches have been
conducted on this task, most prior works paid too much attention to the designs
of networks instead of considering the pathological association for lesions.
Through investigating the pathogenic causes of DR lesions in advance, we found
that certain lesions are closed to specific vessels and present relative
patterns to each other. Motivated by the observation, we propose a relation
transformer block (RTB) to incorporate attention mechanisms at two main levels:
a self-attention transformer exploits global dependencies among lesion
features, while a cross-attention transformer allows interactions between
lesion and vessel features by integrating valuable vascular information to
alleviate ambiguity in lesion detection caused by complex fundus structures. In
addition, to capture the small lesion patterns first, we propose a global
transformer block (GTB) which preserves detailed information in deep network.
By integrating the above blocks of dual-branches, our network segments the four
kinds of lesions simultaneously. Comprehensive experiments on IDRiD and DDR
datasets well demonstrate the superiority of our approach, which achieves
competitive performance compared to state-of-the-arts.
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