Context-Aware Contrastive Learning for Virtual IHC Staining With Inconsistent Image Pairs.

Area of Science:

• Digital pathology
• Computational histopathology
• Medical image analysis

Background:

• Virtual immunohistochemical (IHC) staining converts hematoxylin and eosin (H&E) images to IHC images, a promising digital histopathology technique.
• Current methods often use adjacent tissue sections for training, leading to misalignment and tissue loss issues that limit accuracy.

Purpose of the Study:

• To develop a novel framework, ConCLR, for virtual IHC staining that effectively handles inconsistently paired H&E and IHC patches.
• To improve the accuracy and robustness of virtual IHC staining models despite challenges like tissue misalignment and loss.

Main Methods:

• Proposed a two-stage framework, ConCLR, utilizing context-aware contrastive learning.
• Stage 1: Similarity-guided mini-patch sampling (SGMS) module to find similar mini-patches for contrastive learning despite mild misalignment.
• Stage 2: Context-aware adaptive refinement module to address significant inconsistencies by expanding the search range for positive samples.

Main Results:

• ConCLR demonstrated effectiveness across two network backbones and four virtual IHC staining tasks.
• Evaluations included qualitative and quantitative assessments of staining results and downstream diagnostic performance.
• A new PanCK-NSCLC dataset with improved tissue alignment was created to further advance virtual IHC staining.

Conclusions:

• The ConCLR framework successfully addresses limitations of existing virtual IHC staining methods caused by tissue inconsistencies.
• The context-aware contrastive learning approach significantly improves the accuracy of virtual IHC staining.
• The developed dataset and methodology pave the way for more reliable digital histopathology tools.