Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

TL;DR

This paper introduces a Bayesian deep learning approach using variational inference for brain tumor segmentation, providing both accurate predictions and uncertainty estimates to improve interpretability and safety in medical imaging.

Contribution

It presents a novel encoder-decoder model that incorporates variational inference to quantify uncertainty in biomedical image segmentation tasks.

Findings

Effective segmentation of brain tumors on BRATS dataset.

Model captures both aleatoric and epistemic uncertainties.

Achieves high Dice Similarity Coefficient and IOU scores.

Abstract

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.