What question did this study set out to answer?

The aim is to create a multi-task learning framework to improve colorectal cancer diagnosis by integrating tissue segmentation and tumor grading.

May 15, 2026

Unified Multi-task Learning for Colorectal Cancer Diagnosis via Uncertainty-aware Routing, Cross-task Consistency, and Resolution-adaptive Training Process

Puntos clave

The aim is to create a multi-task learning framework to improve colorectal cancer diagnosis by integrating tissue segmentation and tumor grading.
Utilized Hierarchical Uncertainty-Gated Task Routing for dynamic feature allocation based on uncertainty.
Employed Cross-Task Consistency Attention Matrix to ensure coherence between segmentation and grading tasks.
Implemented a Curriculum-Based Multi-Resolution Task Cascade for progressive training across varying image resolutions.
Achieved a 6.8% increase in tumor grading AUC.
Improved segmentation Dice score by 3.5%.
Reduced model parameters by 27% and decreased inter-observer variability by 14.3%.

Resumen

Introduction: The study aims to develop a unified multi-task learning framework for colorectal cancer diagnosis using whole-slide histopathology images. Specifically, it targets joint tissue segmentation and tumor grading, enhancing label efficiency, robustness, and generalization, while minimizing the redundancy and annotation burden typically associated with treating these tasks independently. Methods: 1. Hierarchical Uncertainty-Gated Task Routing (HUGTR): Dynamically allocates encoder features to decoders based on aleatoric and epistemic uncertainty. 2. Cross-Task Consistency Attention Matrix (CTCAM): Enforces spatial coherence between segmentation and grading by aligning attention maps across tasks. 3. Adaptive Label Denoising with Structural Priors (ALDSP): Employs graph convolutional autoencoders guided by structural priors from tissue segmentation. 4. Contrastive Segmentation-Grading Latent Embedding (CSGLE): Implements a two-headed contrastive learning module to align the latent representation spaces of segmentation. 5. Curriculum-Based Multi-Resolution Task Cascade (CMRTC): Trains the model progressively from low to high resolution images, adapting it to different spatial scales and complexity levels in whole-slide images (WSIs). Results: 6.8% increase in tumor grading AUC, +3.5% improvement in segmentation Dice score, and 27% reduction in model parameters,14.3% decrease in inter-observer variability Discussion: The combination of uncertainty-aware routing, cross-task alignment, and label denoising significantly enhances both diagnostic precision and model efficiency. By treating segmentation and grading as interrelated rather than isolated tasks, the model better captures shared pathological patterns and domain priors. The incorporation of contrastive learning and multi-resolution training further supports generalization across patients and datasets. Conclusion: This unified multi-task framework sets a new benchmark in histopathological analysis for colorectal cancer by effectively integrating tissue segmentation and tumor grading. The method’s innovations enable better use of annotations, improved diagnostic consistency, and enhanced scalability, positioning it as a robust AI tool in pathology workflows.

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