CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities

• URL: http://arxiv.org/abs/2604.13409v1
• Date: Wed, 15 Apr 2026 02:20:09 GMT
• Title: CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities
• Abstract summary: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data.<n>This vulnerability stems primarily from modality bias, where models exploit spurious correlations as shortcuts rather than learning true anatomical structures.<n>We propose CausalDisenSeg, a novel Structural Causal Model (SCM)-grounded framework that achieves robust segmentation via causality-guided disentanglement and counterfactual reasoning.
• Score: 14.27898790157402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. This vulnerability stems primarily from modality bias, where models exploit spurious correlations as shortcuts rather than learning true anatomical structures. Existing feature fusion methods fail to fundamentally eliminate this dependency. To address this, we propose CausalDisenSeg, a novel Structural Causal Model (SCM)-grounded framework that achieves robust segmentation via causality-guided disentanglement and counterfactual reasoning. We reframe the problem as isolating the anatomical Causal Factor from the stylistic Bias Factor. Our framework implements a three-stage causal intervention: (1) Explicit Causal Disentanglement: A Conditional Variational Autoencoder (CVAE) coupled with an HSIC constraint mathematically enforces statistical orthogonality between anatomical and style features. (2) Causal Representation Reinforcement: A Region Causality Module (RCM) explicitly grounds causal features in physical tumor regions. (3) Counterfactual Reasoning: A dual-adversarial strategy actively suppresses the residual Natural Direct Effect (NDE) of the bias, forcing its spatial attention to be mutually exclusive from the causal path. Extensive experiments on the BraTS 2020 dataset demonstrate that CausalDisenSeg significantly outperforms state-of-the-art methods in accuracy and consistency across severe missing-modality scenarios. Furthermore, cross-dataset evaluation on BraTS 2023 under the same protocol yields a state-of-the-art macro-average DSC of 84.49.

Related papers

• CausalArena: Benchmarking Causal Discovery in the Foundation Model Era [47.81267771630766]
  CausalArena is a unified benchmark for causal discovery under a common protocol.<n>We show that strong performance in one benchmark regime does not reliably transfer to others.
  arXiv  Detail & Related papers  (2026-09-10T17:53:15Z)
• C2RM-Seg: Causal Counterfactual Reasoning with Structural-Semantic Priors for Weakly Supervised Histopathological Tissue Segmentation [21.55068744950547]
  C$2$RM-Seg is a two-stage framework that integrates causal pseudo-label refinement with structure-aware semantic enhancement.<n>For classification, we introduce a Causal Counterfactual Reasoning Module (C$2$RM) that decomposes features into latent factors.<n>For segmentation, we design a Dual-Path Structural-Semantic Architecture that combines fine-grained structural features from ResNeSt with global semantic priors from a frozen DINOV3 foundation model.
  arXiv  Detail & Related papers  (2026-06-24T07:44:26Z)
• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• MedCausalX: Adaptive Causal Reasoning with Self-Reflection for Trustworthy Medical Vision-Language Models [10.466505116993451]
  Existing medical chain-of-thought models lack explicit mechanisms to represent and enforce causal reasoning.<n>We propose MedCausalX, an end-to-end framework explicitly models causal reasoning chains in medical VLMs.<n>We show that MedCausalX consistently outperforms state-of-the-art methods, improving diagnostic consistency by +5.4 points, reducing hallucination by over 10 points, and attaining top spatial grounding IoU.
  arXiv  Detail & Related papers  (2026-03-24T11:28:15Z)
• Disentangling History and Propagation Dependencies in Cross-Subject Knee Contact Stress Prediction Using a Shared MeshGraphNet Backbone [0.8283940114367679]
  It remains unclear whether the dominant source of prediction uncertainty arises from temporal history dependence or spatial propagation dependence.<n>A dataset of running trials from nine subjects was constructed using an OpenSim-FEBio workflow.<n>Models incorporating history encoding significantly outperformed the baseline MGN and ModMGN in global accuracy and spatial consistency.
  arXiv  Detail & Related papers  (2026-01-13T08:15:57Z)
• CausalMamba: Scalable Conditional State Space Models for Neural Causal Inference [3.1641441457189496]
  We introduce CausalMamba, a scalable framework that addresses fundamental limitations in fMRI-based causal inference.<n>Our approach decomposes this complex inverse problem into two tractable stages: BOLD deconvolution to recover latent neural activity, followed by causal graph inference.
  arXiv  Detail & Related papers  (2025-10-20T09:04:25Z)
• A Neural Framework for Generalized Causal Sensitivity Analysis [78.71545648682705]
  We propose NeuralCSA, a neural framework for causal sensitivity analysis. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest.
  arXiv  Detail & Related papers  (2023-11-27T17:40:02Z)
• CASR: Refining Action Segmentation via Marginalizing Frame-levle Causal Relationships [23.035761299444953]
  We propose Causal Abstraction Refiner (CASR), which can refine TAS results from various models by enhancing video causality. We define the frame-level casual model and segment-level causal model, so that the causal adjacency matrix constructed from marginalized frame-level causal relationships has the ability to represent the segmnet-level causal relationships.
  arXiv  Detail & Related papers  (2023-11-21T07:28:51Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• The Causal Neural Connection: Expressiveness, Learnability, and Inference [125.57815987218756]
  An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
  arXiv  Detail & Related papers  (2021-07-02T01:55:18Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

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.