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Title: Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.  more » « less
Award ID(s):
2028361
PAR ID:
10148041
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 28th International Joint Conference on Artificial Intelligence
Page Range / eLocation ID:
2894 to 2900
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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