Multi-Stain Learning for Robust Neuropathology Evaluation
About
Postmortem histopathology remains the gold standard for diagnosing neurodegenerative diseases, yet the diagnostic workflows are often constrained by variable stain availability. We introduce a multimodal deep learning framework for whole slide image analysis that models inter-stain relationships and performs robustly under missing-modality conditions. Through cross-stain representation learning, the framework achieves more reliable and accessible diagnostic performance, offering a step toward data-efficient and resource-flexible digital pathology.
Speaker
Lingyi Xu
Lingyi Xu is a Ph.D. student in the Faculty of Computing & Data Sciences at Boston University. She works with Professor Vijaya B. Kolachalama to seek solutions to data missingness in multimodal learning. Her work investigates how different data modalities can be represented and aligned to make learning more adaptable and their relationships more interpretable.