Title: UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION. 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.