CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis

Yajie Zhang, Yu An Huang, Yao Hu, Rui Liu, Jibin Wu, Zhi An Huang, Kay Chen Tan

Research output: Contribution to journalArticlepeer-review

Abstract

Confounding factors inherent in medical images can significantly impact the causal exploration capabilities of deep learning models, resulting in compromised accuracy and diminished generalization performance. In this paper, we present an innovative methodology named CausalMixNet that employs query-mixed intra-attention and key&value-mixed inter-attention to probe causal relationships between input images and labels. For mitigating unobservable confounding factors, CausalMixNet integrates the non-local reasoning module (NLRM) and the key&value-mixed inter-attention (KVMIA) to conduct a front-door adjustment strategy. Furthermore, CausalMixNet incorporates a patch-masked ranking module (PMRM) and query-mixed intra-attention (QMIA) to enhance mediator learning, thereby facilitating causal intervention. The patch mixing mechanism applied to query/(key&value) features within QMIA and KVMIA specifically targets lesion-related feature enhancement and the inference of average causal effect inference. CausalMixNet consistently outperforms existing methods, achieving superior accuracy and F1-scores across in-domain and out-of-domain scenarios on multiple datasets, with an average improvement of 3% over the closest competitor. Demonstrating robustness against noise, gender bias, and attribute bias, CausalMixNet excels in handling unobservable confounders, maintaining stable performance even in challenging conditions.

Original languageEnglish
Article number103581
JournalMedical Image Analysis
Volume103
DOIs
StatePublished - Jul 2025

Keywords

  • Front-door adjustment
  • Medical image diagnosis
  • Patch mixing
  • Unobserved confounders

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