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I'm interested in segmentation models for medical imaging purposes. When I looked at the state of the art, I fell on a paper on a new architecture, Unet++:

UNet++: A Nested U-Net Architecture for Medical Image Segmentation from Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang at Arizona State University

Like Unet, it is has an encoder/decoder architecture with skip connections (adding fine-grained feature maps of the encoder to the decoder). However, in Unet++, the skip connections are nested and dense so that a model can improve its ability to capture fine-grained details.

The second difference with Unet is the use of deep supervision. The papers says that deep supervision enables:

"the model to operate in two modes: 1) accurate mode wherein the outputs from all segmentation branches are averaged; 2) fast mode wherein the final segmentation map is selected from only one of the segmentation branches, the choice of which determines the extent of model pruning and speed gain"

I didn't understand how deep supervision works and what are the benefits in the case of Unet++.

Can you explain how it works? Thank you in advance for your help.

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2 Answers 2

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Partial answer: Quoting a paper

"The advantage of such deep supervision is evident: (1)for small training data and relatively shallower networks, deep supervision functions as a strong “regularization” for classification accuracy and learned features; (2) for large training data and deeper networks deep supervision makes it convenient to exploit the significant performance gains that extremely deep networks can bring by improving otherwise problematic convergence behavior"

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The idea of deep supervision is to add, so called, companion objective functions at each hidden layer of a network and then compute the final loss as the output loss plus the sum of the companion losses. The idea was introduced in this paper:

@inproceedings{lee2015deeply,
  title={Deeply-supervised nets},
  author={Lee, Chen-Yu and Xie, Saining and Gallagher, Patrick and Zhang, Zhengyou and Tu, Zhuowen},
  booktitle={Artificial intelligence and statistics},
  pages={562--570},
  year={2015},
  organization={PMLR}
}
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