CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts

• URL: http://arxiv.org/abs/2510.14273v1
• Date: Thu, 16 Oct 2025 03:45:31 GMT
• Title: CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts
• Authors: Kieu-Anh Truong Thi, Huy-Hieu Pham, Duc-Trong Le,
• Abstract summary: Domain shift in histopathology poses a major challenge to the generalization ability of deep learning models.<n>We propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders.<n>We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains.
• Score: 2.0327514588332996
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models. Existing methods primarily rely on modeling statistical correlations by aligning feature distributions or introducing statistical variation, yet they often overlook causal relationships. In this work, we propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders. Our method implements the front-door principle by designing transformation strategies that explicitly incorporate mediators and observed tissue slides. We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains. As a result, our approach achieved up to a 7% improvement in both the CAMELYON17 dataset and the private histopathology dataset, outperforming existing baselines. These results highlight the potential of causal inference as a powerful tool for addressing domain shift in histopathology image analysis.

Related papers

• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• Coarsening Causal DAG Models [0.0]
  We propose an efficient, provably consistent algorithm for learning abstract causal graphs from interventional data with unknown intervention targets.<n>As proof of concept, we apply our algorithm on synthetic and real datasets with known ground truths.
  arXiv  Detail & Related papers  (2026-01-15T15:56:20Z)
• Causal Debiasing Medical Multimodal Representation Learning with Missing Modalities [6.02318066285653]
  Real-world medical datasets often suffer from missing modalities due to cost, protocol, or patient-specific constraints.<n>Our method consists of two key components: (1) a missingness deconfounding module that approximates causal intervention based on backdoor adjustment and (2) a dual-branch neural network that explicitly disentangles causal features from spurious correlations.
  arXiv  Detail & Related papers  (2025-09-06T06:27:10Z)
• Identifying biological perturbation targets through causal differential networks [23.568795598997376]
  We propose a causality-inspired approach to identify variables responsible for changes to a biological system.<n>First, we infer noisy causal graphs from the observational and interventional data.<n>We then learn to map the differences between these graphs, along with additional statistical features, to sets of variables that were intervened upon.
  arXiv  Detail & Related papers  (2024-10-04T12:48:21Z)
• Large-Scale Targeted Cause Discovery via Learning from Simulated Data [66.51307552703685]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations.<n>We train a neural network using supervised learning on simulated data to infer causality.<n> Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets [28.59271580918754]
  An effective prognostic model could assist physicians in making accurate diagnoses and designing personalized treatment plans.<n>limited data collection, insufficient clinical experience, and privacy and ethical concerns restrict data availability.<n>We present a domain-invariant representation learning method that constructs a transition model between source and target datasets.
  arXiv  Detail & Related papers  (2023-10-11T18:32:21Z)
• Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications [60.26261850082012]
  We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
  arXiv  Detail & Related papers  (2022-09-29T17:44:14Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)

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.