Paper ID: 2408.12974 • Published Aug 23, 2024
Accuracy Improvement of Cell Image Segmentation Using Feedback Former
Hinako Mitsuoka, Kazuhiro Hotta
TL;DR
Get AI-generated summaries with premium
Get AI-generated summaries with premium
Semantic segmentation of microscopy cell images by deep learning is a
significant technique. We considered that the Transformers, which have recently
outperformed CNNs in image recognition, could also be improved and developed
for cell image segmentation. Transformers tend to focus more on contextual
information than on detailed information. This tendency leads to a lack of
detailed information for segmentation. Therefore, to supplement or reinforce
the missing detailed information, we hypothesized that feedback processing in
the human visual cortex should be effective. Our proposed Feedback Former is a
novel architecture for semantic segmentation, in which Transformers is used as
an encoder and has a feedback processing mechanism. Feature maps with detailed
information are fed back to the lower layers from near the output of the model
to compensate for the lack of detailed information which is the weakness of
Transformers and improve the segmentation accuracy. By experiments on three
cell image datasets, we confirmed that our method surpasses methods without
feedback, demonstrating its superior accuracy in cell image segmentation. Our
method achieved higher segmentation accuracy while consuming less computational
cost than conventional feedback approaches. Moreover, our method offered
superior precision without simply increasing the model size of Transformer
encoder, demonstrating higher accuracy with lower computational cost.