Unsupervised bias discovery in medical image segmentation

• URL: http://arxiv.org/abs/2309.00451v1
• Date: Fri, 1 Sep 2023 13:29:26 GMT
• Title: Unsupervised bias discovery in medical image segmentation
• Authors: Nicol\'as Gaggion, Rodrigo Echeveste, Lucas Mansilla, Diego H. Milone, Enzo Ferrante
• Abstract summary: Deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations. We propose a new method to anticipate model biases in biomedical image segmentation in the absence of ground-truth annotations.
• Score: 6.169194620442498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity. In this context, auditing fairness of deep segmentation models becomes crucial. However, such audit process generally requires access to ground-truth segmentation masks for the target population, which may not always be available, especially when going from development to deployment. Here we propose a new method to anticipate model biases in biomedical image segmentation in the absence of ground-truth annotations. Our unsupervised bias discovery method leverages the reverse classification accuracy framework to estimate segmentation quality. Through numerical experiments in synthetic and realistic scenarios we show how our method is able to successfully anticipate fairness issues in the absence of ground-truth labels, constituting a novel and valuable tool in this field.

Related papers

• WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care [1.7819099868722776]
  Automated wound monitoring via mobile image capture can reduce in-person physician visits by enabling remote tracking of wound size. We benchmark state-of-the-art deep learning models from general-purpose vision, medical imaging, and top methods from public wound challenges. We demonstrate how our AI-driven wound size estimation framework, WoundAmbit, can be integrated into a custom telehealth system.
  arXiv  Detail & Related papers  (2025-04-08T16:25:59Z)
• COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images [3.5418498524791766]
  This research is development of a novel counterfactual inpainting approach (COIN) COIN flips the predicted classification label from abnormal to normal by using a generative model. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia.
  arXiv  Detail & Related papers  (2024-04-19T12:09:49Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Anatomically-aware Uncertainty for Semi-supervised Image Segmentation [12.175556059523863]
  Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. Uncertainty estimation methods rely on multiple inferences from the model predictions that must be computed for each training step. This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks.
  arXiv  Detail & Related papers  (2023-10-24T18:03:07Z)
• 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)
• Explainable Semantic Medical Image Segmentation with Style [7.074258860680265]
  We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-10T04:34:51Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Analysing the effectiveness of a generative model for semi-supervised medical image segmentation [23.898954721893855]
  State-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. Semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Deep generative models such as the SemanticGAN are truly viable alternatives to tackle challenging medical image segmentation problems.
  arXiv  Detail & Related papers  (2022-11-03T15:19:59Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)
• A survey on shape-constraint deep learning for medical image segmentation [0.46023882211671957]
  convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmentation. The over dependence of these methods on pixel level classification and regression has been identified early on as a problem. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models are becoming increasingly popular.
  arXiv  Detail & Related papers  (2021-01-19T16:52:10Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)

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.