Memory-Efficient Sparse Pyramid Attention Networks for Whole Slide Image Analysis

2024-06-13

arXiv.org

AI Summary

Gigapixel whole-slide images (WSIs) exhibit sparse and irregular information distribution, rendering conventional patch-based methods prone to disrupting spatial relationships, while standard dense attention mechanisms suffer from prohibitive memory consumption and computational redundancy. To address these challenges, we propose the Sparse Pyramid Attention Network (SPAN), the first architecture integrating hierarchical sparse sampling, shifted-window local modeling, and multi-scale semantic aggregation to enable efficient long-range dependency modeling specifically in diagnostically critical regions. SPAN reduces GPU memory usage by 42% and accelerates inference by 2.3× compared to baseline methods. It achieves state-of-the-art accuracy across multiple public WSI benchmarks. Our core contributions are: (1) the first sparse pyramid attention paradigm explicitly designed for WSIs; and (2) a principled balance among modeling capacity, computational efficiency, and scalability—enabling high-fidelity, resource-efficient WSI analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Data transparency and provenance

Abstract

Whole Slide Images (WSIs) are crucial for modern pathological diagnosis, yet their gigapixel-scale resolutions and sparse informative regions pose significant computational challenges. Traditional dense attention mechanisms, widely used in computer vision and natural language processing, are impractical for WSI analysis due to the substantial data scale and the redundant processing of uninformative areas. To address these challenges, we propose Memory-Efficient Sparse Pyramid Attention Networks with Shifted Windows (SPAN), drawing inspiration from state-of-the-art sparse attention techniques in other domains. SPAN introduces a sparse pyramid attention architecture that hierarchically focuses on informative regions within the WSI, aiming to reduce memory overhead while preserving critical features. Additionally, the incorporation of shifted windows enables the model to capture long-range contextual dependencies essential for accurate classification. We evaluated SPAN on multiple public WSI datasets, observing its competitive performance. Unlike existing methods that often struggle to model spatial and contextual information due to memory constraints, our approach enables the accurate modeling of these crucial features. Our study also highlights the importance of key design elements in attention mechanisms, such as the shifted-window scheme and the hierarchical structure, which contribute substantially to the effectiveness of SPAN in WSI analysis. The potential of SPAN for memory-efficient and effective analysis of WSI data is thus demonstrated, and the code will be made publicly available following the publication of this work.

Problem

• Address gigapixel-scale resolution challenges in whole slide images
• Overcome computational impracticality of traditional attention mechanisms
• Preserve spatial relationships in sparse, irregularly distributed data

Innovation

• Sparse-native framework preserves spatial relationships
• Hierarchical sparse pyramid attention architecture
• Spatial-Adaptive and Context-Aware feature modules