Efficient Bayesian Uncertainty Estimation for nnU-Net

• URL: http://arxiv.org/abs/2212.06278v3
• Date: Wed, 1 May 2024 06:49:03 GMT
• Title: Efficient Bayesian Uncertainty Estimation for nnU-Net
• Authors: Yidong Zhao, Changchun Yang, Artur Schweidtmann, Qian Tao,
• Abstract summary: We introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.
• Score: 3.8186085899889943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible failure. This can be problematic for large-scale image segmentation applications, where data are heterogeneous and nnU-Net may fail without notice. In this work, we introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We propose a highly effective scheme for posterior sampling of weight space for Bayesian uncertainty estimation. Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use. Additionally, we boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. We applied our method on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved uncertainty estimation over a range of baseline methods. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.

Related papers

• Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation [3.0665936758208447]
  Deep learning methods have achieved state-of-theart performance for many medical image segmentation tasks. Recent studies show that deep neural networks (DNNs) can be miscalibrated and overconfident, leading to "silent failures" We propose a Bayesian learning framework using Hamiltonian Monte Carlo (HMC), tempered by cold posterior (CP) to accommodate medical data augmentation.
  arXiv  Detail & Related papers  (2024-03-04T18:47:56Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Unsupervised Deep Learning Meets Chan-Vese Model [77.24463525356566]
  We propose an unsupervised image segmentation approach that integrates the Chan-Vese (CV) model with deep neural networks. Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space.
  arXiv  Detail & Related papers  (2022-04-14T13:23:57Z)
• Trustworthy Medical Segmentation with Uncertainty Estimation [0.7829352305480285]
  This paper introduces a new Bayesian deep learning framework for uncertainty quantification in segmentation neural networks. We evaluate the proposed framework on medical image segmentation data from Magnetic Resonances Imaging and Computed Tomography scans. Our experiments on multiple benchmark datasets demonstrate that the proposed framework is more robust to noise and adversarial attacks as compared to state-of-the-art segmentation models.
  arXiv  Detail & Related papers  (2021-11-10T22:46:05Z)
• A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data [24.12765716392381]
  We propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML) Via DML, the framework learns a multimodal mixture representation for each category, and performs dense predictions based on cosine distances between the pixels' deep embeddings and the category representations. In our experiments on brain MRI and abdominal CT datasets, the proposed framework achieves superior performances for low-shot segmentation towards standard DNN-based (3D U-Net) and classical registration-based (ANTs) methods.
  arXiv  Detail & Related papers  (2021-10-18T13:01:06Z)
• Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation [92.9634065964963]
  We present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet) based on our uncertainty estimation and separate self-training strategy. Compared with the current state of the art, our CoraNet has demonstrated superior performance.
  arXiv  Detail & Related papers  (2021-10-17T08:49:33Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations [59.65174244047216]
  We propose an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions. This network, when augmented with U-Net, results in significant improvements in the case of segmenting small anatomical landmarks. We evaluate the proposed method on the task of brain anatomy segmentation from 2D Ultrasound of preterm neonates.
  arXiv  Detail & Related papers  (2020-06-08T18:59:24Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.