LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computational Pathology · Depth: Expert, long

Summary

The LRMIL (Low-Resolution Multiple Instance Learning) framework addresses critical limitations in whole slide image (WSI) analysis for digital pathology, specifically the computational overhead and inability to capture global cues associated with high-resolution (HR) patch processing. LRMIL employs a two-stage knowledge distillation strategy to transfer HR knowledge to low-resolution (LR) representations. The first stage involves patch-level cross-resolution distillation, aligning LR patch embeddings with HR representations. The second stage trains an LR-based MIL model using both slide-level supervision and guidance from an HR-based teacher MIL model. At inference, LRMIL operates exclusively on LR patches, substantially reducing data preprocessing and computational costs. Experiments on multiple WSI benchmarks demonstrate that LRMIL consistently outperforms state-of-the-art MIL methods, achieving more efficient inference by over an order of magnitude while maintaining superior performance.

Key takeaway

For Machine Learning Engineers developing WSI analysis pipelines, LRMIL offers a significant pathway to overcome computational bottlenecks. You should consider implementing its two-stage knowledge distillation to enable accurate, low-resolution inference. This approach substantially reduces preprocessing and feature extraction costs, making your models more scalable and practical for real-world clinical deployment without sacrificing diagnostic performance.

Key insights

LRMIL efficiently analyzes whole slide images by distilling high-resolution knowledge into low-resolution models for faster inference.

Principles

• Decouple training and inference resolutions.
• Transfer fine-grained HR semantics to LR features.
• Align attention patterns across resolutions.

Method

LRMIL uses a two-stage distillation: patch-level cross-resolution to train an LR encoder, then slide-level distillation to train an LR MIL model with HR teacher guidance.

In practice

• Deploy LR-only models for WSI inference.
• Reduce WSI preprocessing overhead.
• Improve scalability in clinical pathology.

Topics

• Whole Slide Imaging
• Multiple Instance Learning
• Knowledge Distillation
• Digital Pathology
• Computational Efficiency
• Low-Resolution Inference

Code references

• hvcl/LRMIL

Best for: AI Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.