MIDL 2020, P011, Nguyen et al. Spotlight presentation
Channel: MIDL Society
Date: 2020-07-06

P011 - End-to-end learning of convolutional neural net and dynamic programming for left ventricle segmentation

Nhat M. Nguyen, Nilanjan Ray

Conference page: https://2020.midl.io/papers/nguyen20a.html
PDF: https://openreview.net/pdf?id=_4_RPMYWN

Abstract: Differentiable programming is able to combine different functions or modules in a data processing pipeline with the goal of applying gradient descent-based end-to-end learning or optimization. A significant impediment to differentiable programming is the non-differentiable nature of some functions.  We propose to overcome this difficulty by using neural networks to approximate such modules.  An approximating neural network provides synthetic gradients (SG) for backpropagation across a non-differentiable module.  Our design is grounded on a well-known theory that gradient of an approximating neural network can approximate a sub-gradient of a weakly differentiable function.  We apply SG to combine convolutional neural  network  (CNN)  with  dynamic  programming  (DP)  in  end-to-end  learning  for  segmenting left ventricle from short axis view of heart MRI. Our experiments show that end-to-end combination of CNN and DP requires fewer labeled images to achieve a significantly better segmentation accuracy than using only CNN.