MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis

• URL: http://arxiv.org/abs/2511.22990v1
• Date: Fri, 28 Nov 2025 08:51:00 GMT
• Title: MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis
• Authors: Louisa Fay, Hajer Reguigui, Bin Yang, Sergios Gatidis, Thomas Küstner,
• Abstract summary: MIMM-X is a framework that disentangles causal features from multiple spurious correlations.<n>It enables predictions based on true underlying causal relationships rather than dataset-specific shortcuts.
• Score: 5.80701778605144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models can excel on medical tasks, yet often experience spurious correlations, known as shortcut learning, leading to poor generalization in new environments. Particularly in medical imaging, where multiple spurious correlations can coexist, misclassifications can have severe consequences. We propose MIMM-X, a framework that disentangles causal features from multiple spurious correlations by minimizing their mutual information. It enables predictions based on true underlying causal relationships rather than dataset-specific shortcuts. We evaluate MIMM-X on three datasets (UK Biobank, NAKO, CheXpert) across two imaging modalities (MRI and X-ray). Results demonstrate that MIMM-X effectively mitigates shortcut learning of multiple spurious correlations.

Related papers

• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages [8.610253537046692]
  We propose a novel framework for brain age estimation utilizing a sex-aware adversarial variational autoencoder (SA-AVAE)<n>We decompose the latent space into modality-specific codes and shared codes to represent complementary and common information across modalities.<n>We incorporate sex information into the learned latent code, enabling the model to capture sex-specific aging patterns for brain age estimation.
  arXiv  Detail & Related papers  (2024-12-07T12:10:29Z)
• MACAW: A Causal Generative Model for Medical Imaging [1.0653740810975534]
  This work introduces a new causal generative architecture named Masked Causal Flow (MACAW) for neuroimaging applications.<n>The feasibility of the proposed method was first evaluated using synthetic data and then using MRI brain data from more than 23000 participants of the UK biobank study.
  arXiv  Detail & Related papers  (2024-12-03T23:05:41Z)
• Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging [7.917858344544847]
  Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. Deep learning models tend to learn spurious correlations instead of causally related features, limiting their generalizability to new and unseen data. This problem can be addressed by minimizing dependence measures between intermediate representations of task-related and non-task-related variables.
  arXiv  Detail & Related papers  (2024-07-26T14:54:16Z)
• MMRel: A Relation Understanding Benchmark in the MLLM Era [72.95901753186227]
  Multi-Modal Relation Understanding (MMRel) is a benchmark that features large-scale, high-quality, and diverse data on inter-object relations. MMRel is ideal for evaluating MLLMs on relation understanding, as well as for fine-tuning MLLMs to enhance relation comprehension capability.
  arXiv  Detail & Related papers  (2024-06-13T13:51:59Z)
• Discovering and Reasoning of Causality in the Hidden World with Large Language Models [109.62442253177376]
  We develop a new framework termed Causal representatiOn AssistanT (COAT) to propose useful measured variables for causal discovery.<n>Instead of directly inferring causality with Large language models (LLMs), COAT constructs feedback from intermediate causal discovery results to LLMs to refine the proposed variables.
  arXiv  Detail & Related papers  (2024-02-06T12:18:54Z)
• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Advancing Radiograph Representation Learning with Masked Record Modeling [52.04899592688968]
  We formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM) MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning.
  arXiv  Detail & Related papers  (2023-01-30T18:33:32Z)
• Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification [10.403614735252503]
  Existing MIML methods suffer from relatively low accuracy and training efficiency due to several issues. BMIML is highly competitive to (or even better than) existing methods in accuracy and much faster than most MIML methods even for large medical image data sets.
  arXiv  Detail & Related papers  (2022-09-06T16:28:54Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [77.78624443410216]
  We propose improved FDAM algorithms for detecting heterogeneous chest data. A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level. Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
  arXiv  Detail & Related papers  (2021-02-09T04:05:19Z)
• Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia [11.100316178148994]
  We introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ) We propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality.
  arXiv  Detail & Related papers  (2020-01-06T18:46:41Z)

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.