The Role of Subgroup Separability in Group-Fair Medical Image Classification

• URL: http://arxiv.org/abs/2307.02791v1
• Date: Thu, 6 Jul 2023 06:06:47 GMT
• Title: The Role of Subgroup Separability in Group-Fair Medical Image Classification
• Authors: Charles Jones, M\'elanie Roschewitz, Ben Glocker
• Abstract summary: We find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.
• Score: 18.29079361470428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate performance disparities in deep classifiers. We find that the ability of classifiers to separate individuals into subgroups varies substantially across medical imaging modalities and protected characteristics; crucially, we show that this property is predictive of algorithmic bias. Through theoretical analysis and extensive empirical evaluation, we find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.

Related papers

• Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data [2.7436483977171333]
  This study aims to investigate the effectiveness of using the backbone of Foundation Models as an embedding extractor. We propose utilizing these groups in different stages of bias mitigation, including pre-processing, in-processing, and evaluation.
  arXiv  Detail & Related papers  (2024-08-28T20:35:38Z)
• On Biases in a UK Biobank-based Retinal Image Classification Model [0.0]
  We explore whether disparities are present in the UK Biobank fundus retinal images by training and evaluating a disease classification model on these images. We find substantial differences despite strong overall performance of the model. We find that these methods are largely unable to enhance fairness, highlighting the need for better bias mitigation methods tailored to the specific type of bias.
  arXiv  Detail & Related papers  (2024-07-30T10:50:07Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• (Predictable) Performance Bias in Unsupervised Anomaly Detection [3.826262429926079]
  Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. Our study quantified the disparate performance of UAD models against certain demographic subgroups.
  arXiv  Detail & Related papers  (2023-09-25T14:57:43Z)
• Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes [0.0]
  This study presents a machine learning pipeline for clinical data classification in the context of a 30-day readmission problem. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.
  arXiv  Detail & Related papers  (2023-04-12T17:09:38Z)
• 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)
• Explaining medical AI performance disparities across sites with confounder Shapley value analysis [8.785345834486057]
  Multi-site evaluations are key to diagnosing such disparities. Our framework provides a method for quantifying the marginal and cumulative effect of each type of bias on the overall performance difference. We demonstrate its usefulness in a case study of a deep learning model trained to detect the presence of pneumothorax.
  arXiv  Detail & Related papers  (2021-11-12T18:54:10Z)
• 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)
• LOGAN: Local Group Bias Detection by Clustering [86.38331353310114]
  We argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. We propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region.
  arXiv  Detail & Related papers  (2020-10-06T16:42:51Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• 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)

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.