DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology

2025-10-06

AI Summary

High-cost whole-slide imaging (WSI) scanners hinder digital pathology adoption in resource-limited settings; existing low-cost alternatives suffer from unstable focus, prolonged autofocus latency, and poor generalizability. This paper introduces DeepAf—the first spatiospectral autofocus framework supporting single-image input—integrating spatial convolutional and spectral feature extraction to enable end-to-end focal plane prediction and adaptive microscope parameter control. DeepAf significantly improves cross-tissue, cross-staining, and cross-laboratory generalizability: it achieves a focal accuracy of 0.18 μm within the same laboratory, an inter-laboratory misfocus rate of only 0.72%, and places 90% of predictions within the depth of field. Compared to conventional focus stacking, it accelerates autofocus by 80% and reduces training data requirements by 50%. Clinical validation on 536 ×4 brain tissue slides yields an AUC of 0.90 for cancer classification.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Auto ML and Hyperparameter Tuning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitations: automated microscopes struggle with consistent focus across varying tissue morphology, traditional auto-focus methods require time-consuming focal stacks, and existing deep-learning approaches either need multiple input images or lack generalization capability across tissue types and staining protocols. We introduce a novel automated microscopic system powered by DeepAf, a novel auto-focus framework that uniquely combines spatial and spectral features through a hybrid architecture for single-shot focus prediction. The proposed network automatically regresses the distance to the optimal focal point using the extracted spatiospectral features and adjusts the control parameters for optimal image outcomes. Our system transforms conventional microscopes into efficient slide scanners, reducing focusing time by 80% compared to stack-based methods while achieving focus accuracy of 0.18 μm on the same-lab samples, matching the performance of dual-image methods (0.19 μm) with half the input requirements. DeepAf demonstrates robust cross-lab generalization with only 0.72% false focus predictions and 90% of predictions within the depth of field. Through an extensive clinical study of 536 brain tissue samples, our system achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans. This results in a comprehensive hardware-software design enabling accessible, real-time digital pathology in resource-constrained settings while maintaining diagnostic accuracy.

Problem

• Automated microscopes struggle with consistent focus across tissues
• Traditional auto-focus methods require time-consuming focal stacks
• Existing deep-learning approaches lack generalization across tissue types

Innovation

• Hybrid architecture combining spatial and spectral features
• Single-shot focus prediction using spatiospectral auto-focus framework
• Automated microscope system regressing distance to optimal focal point