RANGER: Sparsely-Gated Mixture-of-Experts with Adaptive Retrieval Re-ranking for Pathology Report Generation

2026-03-04

AI Summary

Pathology report generation faces significant challenges due to the large scale of whole-slide images and strong morphological heterogeneity. Existing approaches, which rely on homogeneous decoders and static knowledge retrieval, struggle to achieve specialized generation and are prone to introducing noise. To address these limitations, this work proposes RANGER, a novel framework that integrates a sparse-gated mixture-of-experts (MoE) decoder with an adaptive retrieval re-ranking mechanism. The MoE decoder dynamically activates task-specific experts to accommodate diverse diagnostic patterns, while the re-ranking module enhances semantic alignment between retrieved knowledge and visual features. Evaluated on the PathText-BRCA dataset, RANGER substantially outperforms current methods, achieving BLEU-1 to BLEU-4 scores of 0.4598–0.1435, a METEOR score of 0.1883, and a ROUGE-L score of 0.3038.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Pathology report generation remains a relatively under-explored downstream task, primarily due to the gigapixel scale and complex morphological heterogeneity of Whole Slide Images (WSIs). Existing pathology report generation frameworks typically employ transformer architectures, relying on a homogeneous decoder architecture and static knowledge retrieval integration. Such architectures limit generative specialization and may introduce noisy external guidance during the report generation process. To address these limitations, we propose RANGER, a sparsely-gated Mixture-of-Experts (MoE) framework with adaptive retrieval re-ranking for pathology report generation. Specifically, we integrate a sparsely gated MoE into the decoder, along with noisy top-$k$ routing and load-balancing regularization, to enable dynamic expert specialization across various diagnostic patterns. Additionally, we introduce an adaptive retrieval re-ranking module that selectively refines retrieved memory from a knowledge base before integration, reducing noise and improving semantic alignment based on visual feature representations. We perform extensive experiments on the PathText-BRCA dataset and demonstrate consistent improvements over existing approaches across standard natural language generation metrics. Our full RANGER model achieves optimal performance on PathText dataset, reaching BLEU-1 to BLEU-4 scores of 0.4598, 0.3044, 0.2036, and 0.1435, respectively, with METEOR of 0.1883, and ROUGE-L of 0.3038, validating the effectiveness of dynamic expert routing and adaptive knowledge refinement for semantically grounded pathology report generation.

Problem

• Pathology report generation
• Whole Slide Images
• Mixture-of-Experts
• Knowledge retrieval
• Morphological heterogeneity

Innovation

• Mixture-of-Experts
• adaptive retrieval re-ranking
• sparse gating
• pathology report generation
• dynamic expert routing