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Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier
Meghdad Sabouri Rad1, Junze Vincent Huang2, Mohammad Mehdi Hosseini1
1Department of Pathology, SUNY Upstate Medical University, 13210, Syracuse, USA.
Journal of Imaging Informatics in Medicine
|March 17, 2026
Summary
This study introduces a novel framework for robust deep learning-based lung adenocarcinoma subtyping, significantly improving accuracy and reducing errors in classifying invasive subtypes from whole-slide images.
Area of Science:
- Computational pathology
- Medical imaging analysis
- Artificial intelligence in oncology
Background:
- Deep learning models for invasive lung adenocarcinoma subtyping are susceptible to real-world imaging variations.
- Accurate subtyping is crucial for effective lung cancer treatment and prognosis.
Purpose of the Study:
- To develop a robust deep learning framework for invasive lung adenocarcinoma subtyping resistant to imaging perturbations.
- To enhance the accuracy and reliability of automated subtyping using whole-slide images.
Main Methods:
- Implemented a margin consistency framework integrating attention-weighted aggregation and margin-aware training.
- Introduced Perturbation Fidelity scoring with Bayesian-optimized parameters to mitigate feature over-clustering.
- Evaluated Vision Transformer-Large and ResNet101 models on the BMIRDS-LUAD dataset.
Main Results:
- Achieved significant error reduction in subtyping accuracy for both Vision Transformer-Large (40%) and ResNet101 (50%).
- Demonstrated strong feature-logit space alignment with high Kendall correlations (0.88 training, 0.64 validation).
- Attained excellent performance across all five subtypes with area under ROC curves exceeding 0.99.
Conclusions:
- The proposed margin consistency framework enhances the robustness and accuracy of deep learning for lung adenocarcinoma subtyping.
- The method shows promise for clinical application, though domain adaptation research is needed to address performance variations across institutions.
