Deep Learning (DL) holds great promise in reshaping the healthcare industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-distribution inputs is ailing their deployment in the clinic. Most models produce point estimates without further information about model uncertainty or confidence. This paper introduces a new Bayesian DL framework for uncertainty quantification in segmentation neural networks: SUPER-Net: trustworthy medical image Segmentation with Uncertainty Propagation in Encoder-decodeR Networks. SUPER-Net analytically propagates, using Taylor series approximations, the first two moments (mean and covariance) of the posterior distribution of the model parameters across the nonlinear layers. In particular, SUPER-Net simultaneously learns the mean and covariance without expensive post-hoc Monte Carlo sampling or model ensembling. The output consists of two simultaneous maps: the segmented image and its pixelwise uncertainty map, which corresponds to the covariance matrix of the predictive distribution. We conduct an extensive evaluation of SUPER-Net on medical image segmentation of Magnetic Resonances Imaging and Computed Tomography scans under various noisy and adversarial conditions. Our experiments on multiple benchmark datasets demonstrate that SUPER-Net is more robust to noise and adversarial attacks than state-of-the-art segmentation models. Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks. Perhaps more importantly, the model exhibits the ability of self-assessment of its segmentation decisions, notably when making erroneous predictions due to noise or adversarial examples.
翻译:深学习( DL) 因其精密性、效率和客观性,在重塑医疗保健行业方面有着巨大的希望。 但是, DL 模型对于杂音和分配外投入的微弱性正在限制其在诊所的部署。 大多数模型在不提供关于模型不确定性或信心的进一步信息的情况下生成点估计数。 本文引入了一个新的 Bayesian DL 框架,用于分解神经网络的不确定性量化: SUPER-Net: 在 Eccoder- decodR 网络中, 具有不确定性的可信赖的医疗图像分解与不稳定性分解。 SUPER-Net 分析传播, 使用泰勒序列近似值, 将模型的表面分布在非线性层中( 平均和易变异性), 制作模型的上头两个瞬间( 度和易变异性) 模型在非线性线性数据分析中同时学习了平均值和共变异性。 在磁性测试中, 演示了各种磁性数据对比性测试的模型, 演示了各种磁性模型。