ProCausal-WS: Weakly Supervised Causal Representation Learning Driven Interpretable Prostate Cancer Diagnosis.

Area of Science:

• Computational biology
• Medical informatics
• Causal inference

Background:

• Current prostate cancer diagnosis models struggle with complex, nonlinear data relationships.
• Existing deep learning methods require extensive expert annotations and lack counterfactual reasoning capabilities.

Purpose of the Study:

• To introduce ProCausal-WS, a weakly supervised causal representation learning framework for prostate cancer diagnosis.
• To overcome limitations of existing linear models and deep learning approaches by integrating causal inference and representation learning.

Main Methods:

• Utilized an invertible flow causal encoder for mapping multimodal data to interpretable causal factors.
• Incorporated an exogenous clinical intervention module for simulating treatment scenarios and generating counterfactual predictions.
• Employed a weakly supervised alignment mechanism combining contrastive learning with projection heads for semantic factor identification.

Main Results:

• Achieved high accuracy in clinical causal concept identification (92.3% on TCGA-PRAD, 89.6% on PANDA) with minimal annotations (8% and 5%, respectively).
• Significantly reduced intervention mean-squared error (0.018 on TCGA-PRAD), outperforming baselines.
• Demonstrated robust cross-dataset generalization and high biological plausibility (89.6%) of counterfactual predictions, validated by longitudinal consistency analysis.

Conclusions:

• ProCausal-WS offers a powerful, weakly supervised approach for causal representation learning in prostate cancer.
• The framework enhances diagnostic accuracy, enables counterfactual reasoning, and generalizes well across different data sources.
• This method holds promise for improving computational approaches in precision oncology.