论文标题
使用生物医学图像分割的变异推断的不确定性定量
Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation
论文作者
论文摘要
由卷积神经网络激励的深度学习在一系列医学成像问题（例如图像分类，图像分割，图像合成等）中取得了非常成功的态度，但是对于验证和可解释性，我们不仅需要模型做出的预测，而且还需要在做出这些预测的同时它的自信心。这对于人们接受安全的关键应用很重要。在这项工作中，我们使用了基于变异推理技术的编码器解码器结构来分割脑肿瘤图像。我们使用骰子相似性系数（DSC）和联合（IOU）的交叉点评估了公开可用的BRAT数据集的工作。我们的模型能够以原则上的贝叶斯方式考虑脑瘤的脑肿瘤，同时考虑到疾病的不确定性和认知不确定性。
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.
