Test-time Adaptable Neural Networks for Robust Medical Image Segmentation | JRC Workshop 2021
Computer Vision | Day 1
20 April 2021
Speaker: Neerav Karani, ETH Zurich
(collaboration with Ender Konukoglu, ETH Zurich)
Performance of convolutional neural networks (CNNs) used for medical image analyses degrades markedly when training and test images differ in terms of their acquisition details, such as the scanner model or the protocol. We tackle this issue for the task of image segmentation by adapting a CNN (C) for each test image. Specifically, we design C as a concatenation of a shallow normalization CNN (N), followed by a deep CNN (S) that segments the normalized image. At test time, we adapt N for each test image, guided by an implicit prior on the predicted labels, which is modeled using an independently trained denoising autoencoder (D). The method is validated on multi-center MRI datasets of 3 anatomies.
Related paper: Test-time adaptable neural networks for robust medical image segmentation
Code on GitHub >
- Date:
- Speakers:
- Neerav Karani
- Affiliation:
- ETH Zurich