Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.11397v1
• Date: Fri, 21 Jul 2023 07:29:38 GMT
• Title: Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation
• Authors: Arne Schmidt and Pablo Morales-\'Alvarez and Rafael Molina
• Abstract summary: We propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk (Pionono) It captures the labeling behavior of each rater with a multidimensional probability distribution to produce probabilistic segmentation predictions. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono.
• Score: 12.594098548008832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk (Pionono). It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions. The model is optimized by variational inference and can be trained end-to-end. It outperforms state-of-the-art models such as STAPLE, Probabilistic U-Net, and models based on confusion matrices. Additionally, Pionono predicts multiple coherent segmentation maps that mimic the rater's expert opinion, which provides additional valuable information for the diagnostic process. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono, making it a powerful tool for medical image analysis.

Related papers

• Contrastive Learning with Counterfactual Explanations for Radiology Report Generation [83.30609465252441]
  We propose a textbfCountertextbfFactual textbfExplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking what if'' scenarios. Experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports.
  arXiv  Detail & Related papers  (2024-07-19T17:24:25Z)
• FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms [60.195642571004804]
  We propose FlowSDF, an image-guided conditional flow matching framework to represent the signed distance function (SDF) By learning a vector field that is directly related to the probability path of a conditional distribution of SDFs, we can accurately sample from the distribution of segmentation masks.
  arXiv  Detail & Related papers  (2024-05-28T11:47:12Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Multi-Modal Evaluation Approach for Medical Image Segmentation [4.989480853499916]
  We propose a novel multi-modal evaluation (MME) approach to measure the effectiveness of different segmentation methods. We introduce new relevant and interpretable characteristics, including detection property, boundary alignment, uniformity, total volume, and relative volume. Our proposed approach is open-source and publicly available for use.
  arXiv  Detail & Related papers  (2023-02-08T15:31:33Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures [12.138198227748353]
  Most segmentation methods simply model a mapping from an image to its single segmentation map and do not take the disagreement of annotators into consideration. We propose a novel variational inference framework to model the distribution of plausible segmentation maps, given a specific MR image.
  arXiv  Detail & Related papers  (2022-01-18T16:33:33Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks [8.219843232619551]
  Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. We advocate for multi-task learning, i.e., training a single model on several different datasets. We show that not only a single CNN learns to automatically recognize the context and accurately segment the organ of interest in each context, but also that such a joint model often has more accurate and better-calibrated predictions.
  arXiv  Detail & Related papers  (2020-04-12T23:42:51Z)

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.