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Avoiding overfitting using unetLayers

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Julius Å
Julius Å 2020-3-6
回答： Srivardhan Gadila 2020-3-14
Hello!
I'm trying to get a unetLayers-network to work on a binary segmentation problem. I'm training the network on patches of CT-images where the goal is to segment the bone pixels from background pixels. Since bones in CT-images have high numerical values compared to background, this shouldn't be a difficult problem. However, my network keeps overfitting after approximately 1-2 epochs each time I train.
I have approximately 23000 training patches. There is a slight class imbalance in the extracted patches. The pixel count is:
{'background'} : 1.6879e+08
{'bone' } : 2.2309e+07
What I've tried so far to avoid overfitting:
  • I tried adding augmentation according to: aug = imageDataAugmenter('RandRotation', [-20, 20], 'RandScale', [0.7 1.5]); as well as other types of augmentation.
  • I tried adding different amounts of 'L2Regularization'.
  • I tried making the 'EnoderDepth' smaller in order to reduce the complexity of the model.
What more can I try to remove the overfitting when I'm training my network on this data using the unetLayers-architecture?

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Srivardhan Gadila
Srivardhan Gadila 2020-3-14
The following are some suggestions:
  1. Since there is a slight class imbalance, try the following: Balance Classes Using Class Weighting
  2. Include dropoutLayer and batchNormalizationLayer in your architecture
  3. Refer to Deep Learning Tips and Tricks

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