Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

Deep learning motivated by convolutional neural networks has been highly successful in a range of medical imaging problems like image classification, image segmentation, image synthesis etc. However for validation and interpretability, not only do we need the predictions made by the model but also how confident it is while making those predictions. This is important in safety critical applications for the people to accept it. In this work, we used an encoder decoder architecture based on variational inference techniques for segmenting brain tumour images. We compare different backbones architectures like U-Net, V-Net and FCN as sampling data from the conditional distribution for the encoder. We evaluate our work on the publicly available BRATS dataset using Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU) as the evaluation metrics. Our model outperforms previous state of the art results while making use of uncertainty quantification in a principled bayesian manner.