Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

• Main Author: Sagar, Abhinav
• Format: Journal Article
• Language: English
• Published: 12-08-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2008.07588

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 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 is able to segment brain
tumours while taking into account both aleatoric uncertainty and epistemic
uncertainty in a principled bayesian manner.