Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz

• URL: http://arxiv.org/abs/2305.07618v3
• Date: Fri, 1 Dec 2023 16:00:14 GMT
• Title: Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz
• Authors: Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo, Hongwei B. Li, Bruce R. Rosen, and Matthew S. Rosen
• Abstract summary: Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. It is essential to assess whether a given input falls within the training data distribution for diagnostic purposes. We propose a method based on the local Lipschitz-based metric to distinguish out-of-distribution images from in-distribution with an area under the curve of 99.94%.
• Score: 9.143327181265976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. However, these trained models encounter unseen data distributions that are widely shifted from training data during deployment. Therefore, it is essential to assess whether a given input falls within the training data distribution for diagnostic purposes. Uncertainty estimation approaches exist but focus on providing an uncertainty map to radiologists, rather than assessing the training distribution fit. In this work, we propose a method based on the local Lipschitz-based metric to distinguish out-of-distribution images from in-distribution with an area under the curve of 99.94%. Empirically, we demonstrate a very strong relationship between the local Lipschitz value and mean absolute error (MAE), supported by a high Spearman's rank correlation coefficient of 0.8475, which determines the uncertainty estimation threshold for optimal model performance. Through the identification of false positives, the local Lipschitz and MAE relationship was used to guide data augmentation and reduce model uncertainty. Our study was validated using the AUTOMAP architecture for sensor-to-image Magnetic Resonance Imaging (MRI) reconstruction. We compare our proposed approach with baseline methods: Monte-Carlo dropout and deep ensembles, and further analysis included MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction using UNET architectures. We show that our approach is applicable to various architectures and learned functions, especially in the realm of medical image reconstruction, where preserving the diagnostic accuracy of reconstructed images remains paramount.

Related papers

• Hierarchical uncertainty estimation for learning-based registration in neuroimaging [10.964653898591413]
  We propose a principled way to propagate uncertainties (epistemic or aleatoric) estimated at the level of spatial location. Experiments show that uncertainty-aware fitting of transformations improves the registration accuracy of brain MRI scans.
  arXiv  Detail & Related papers  (2024-10-11T23:12:16Z)
• DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction [45.00528216648563]
  Diffusion Prior Driven Neural Representation (DPER) is an unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets.
  arXiv  Detail & Related papers  (2024-04-27T12:55:13Z)
• Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction [11.766644467766557]
  Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. It is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model.
  arXiv  Detail & Related papers  (2023-09-25T11:07:25Z)
• PixCUE -- Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification [0.0]
  We introduce a method to estimate uncertainty during MRI reconstruction using a pixel classification framework. We demonstrate that this approach generates uncertainty maps that highly correlate with the reconstruction errors. We conclude that PixCUE is capable of reliably estimating the uncertainty in MRI reconstruction with a minimum additional computational cost.
  arXiv  Detail & Related papers  (2023-02-28T22:26:18Z)
• Iterative Data Refinement for Self-Supervised MR Image Reconstruction [18.02961646651716]
  We propose a data refinement framework for self-supervised MR image reconstruction. We first analyze the reason of the performance gap between self-supervised and supervised methods. Then, we design an effective self-supervised training data refinement method to reduce this data bias.
  arXiv  Detail & Related papers  (2022-11-24T06:57:16Z)
• Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis [48.02011627390706]
  We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:05:06Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Joint reconstruction and bias field correction for undersampled MR imaging [7.409376558513677]
  Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Deep learning schemes are susceptible to differences between the training data and the image to be reconstructed at test time. In this work, we address the sensitivity of the reconstruction problem to the bias field and propose to model it explicitly in the reconstruction.
  arXiv  Detail & Related papers  (2020-07-26T12:58:34Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.