Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

• URL: http://arxiv.org/abs/2305.07883v3
• Date: Sat, 24 Jun 2023 06:50:14 GMT
• Title: Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
• Authors: Shuai Wang, Zipei Yan, Daoan Zhang, Zhongsen Li, Sirui Wu, Wenxuan Chen, Rui Li
• Abstract summary: Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. We propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs.
• Score: 5.415458419290347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. Medical image segmentation is typically formulated as a pixel-wise classification task in which each pixel is classified into a category. However, this formulation ignores the hard-to-classified pixels, e.g., some pixels near the boundary area, as they usually confuse DNNs. In this paper, we first explore that hard-to-classified pixels are associated with high uncertainty. Based on this, we propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs, thereby improving its generalization. We evaluate our method on two popular benchmarks: prostate and fundus datasets. The results of the experiment demonstrate that our method outperforms state-of-the-art methods.

Related papers

• Image-level Regression for Uncertainty-aware Retinal Image Segmentation [3.7141182051230914]
  We introduce a novel Uncertainty-Aware (SAUNA) transform, which adds pixel uncertainty to the ground truth. Our results indicate that the integration of the SAUNA transform and these segmentation losses led to significant performance boosts for different segmentation models.
  arXiv  Detail & Related papers  (2024-05-27T04:17:10Z)
• Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation [5.5575224613422725]
  Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. We introduce a novel approach called the Selective Uncertainty-based AL, avoiding the conventional practice of summing up the metrics of all pixels. Our method showed substantial improvements across five different uncertainty-based methods and two distinct datasets.
  arXiv  Detail & Related papers  (2024-01-29T16:59:39Z)
• NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation [87.50830107535533]
  Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. Current approaches to semi-supervised semantic segmentation work by predicting pseudo-labels for each pixel from a class-wise probability distribution output by a model. In this work, we move one step forward by adapting NPs to semi-supervised semantic segmentation, resulting in a new model called NP-SemiSeg.
  arXiv  Detail & Related papers  (2023-08-05T12:42:15Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation [6.197149831796131]
  We propose CRISP a ContRastive Image for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps.
  arXiv  Detail & Related papers  (2022-06-15T16:56:58Z)
• SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation [52.62441404064957]
  Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the model trained on a labeled source domain. Many methods tend to alleviate noisy pseudo labels, however, they ignore intrinsic connections among cross-domain pixels with similar semantic concepts. We propose Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixel.
  arXiv  Detail & Related papers  (2022-04-19T11:16:29Z)
• KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation [64.03817806316903]
  In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image. In an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images. We propose an end-to-end learning framework, known-region-aware domain alignment (KRADA), to distinguish unknown classes while aligning distributions of known classes in labeled and unlabeled open-world images.
  arXiv  Detail & Related papers  (2021-06-11T08:43:59Z)
• ITSELF: Iterative Saliency Estimation fLexible Framework [68.8204255655161]
  Saliency object detection estimates the objects that most stand out in an image. We propose a superpixel-based ITerative Saliency Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions to be added to the model. We compare ITSELF to two state-of-the-art saliency estimators on five metrics and six datasets.
  arXiv  Detail & Related papers  (2020-06-30T16:51:31Z)
• Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty [32.33791302617957]
  We introduce segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. SSNs can generate multiple spatially coherent hypotheses for a single image. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans.
  arXiv  Detail & Related papers  (2020-06-10T18:06:41Z)

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.