SeATrans: Learning Segmentation-Assisted diagnosis model via Transforme
- URL: http://arxiv.org/abs/2206.05763v1
- Date: Sun, 12 Jun 2022 15:10:33 GMT
- Title: SeATrans: Learning Segmentation-Assisted diagnosis model via Transforme
- Authors: Junde Wu, Huihui Fang, Fangxin Shang, Dalu Yang, Zhaowei Wang, Jing
Gao, Yehui Yang, Yanwu Xu
- Abstract summary: We propose Vision-Assisted diagnosis Transformer (SeATrans) to transfer the segmentation knowledge to the disease diagnosis network.
We first propose an asymmetric multi-scale interaction strategy to correlate each single low-level diagnosis feature with multi-scale segmentation features.
To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.
- Score: 13.63128987400635
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Clinically, the accurate annotation of lesions/tissues can significantly
facilitate the disease diagnosis. For example, the segmentation of optic
disc/cup (OD/OC) on fundus image would facilitate the glaucoma diagnosis, the
segmentation of skin lesions on dermoscopic images is helpful to the melanoma
diagnosis, etc. With the advancement of deep learning techniques, a wide range
of methods proved the lesions/tissues segmentation can also facilitate the
automated disease diagnosis models. However, existing methods are limited in
the sense that they can only capture static regional correlations in the
images. Inspired by the global and dynamic nature of Vision Transformer, in
this paper, we propose Segmentation-Assisted diagnosis Transformer (SeATrans)
to transfer the segmentation knowledge to the disease diagnosis network.
Specifically, we first propose an asymmetric multi-scale interaction strategy
to correlate each single low-level diagnosis feature with multi-scale
segmentation features. Then, an effective strategy called SeA-block is adopted
to vitalize diagnosis feature via correlated segmentation features. To model
the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis
feature based on the segmentation information via the encoder, and then
transfers the embedding back to the diagnosis feature space by a decoder.
Experimental results demonstrate that SeATrans surpasses a wide range of
state-of-the-art (SOTA) segmentation-assisted diagnosis methods on several
disease diagnosis tasks.
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