Flexible multimodal learning for whole slide image analysis
OA Version
Citation
Abstract
Whole slide imaging (WSI) has revolutionized digital pathology, enabling the digitization of entire tissue samples on glass slides into high-resolution images, providing a detailed view of tissue morphology essential for analysis and interpretation. However, practical challenges arise due to the varying availability and cost of different slide staining techniques. Hematoxylin and Eosin (H&E) staining is commonly performed and cost-effective, but it offers limited information compared to more expensive and less frequently conducted immunohistochemical (IHC) staining, which provides specific molecular insights critical for diagnosing conditions like Alzheimer’s disease (AD) and chronic traumatic encephalopathy (CTE). Addressing the need for a comprehensive analysis in the absence of complete modality sets, this research introduces ”MultiStainKD ,” a novel framework designed to handle multimodal WSI data effectively, even when certain modalities, such as IHC, are missing. MultiStainKD employs a combination of computational architectural design and training strategies, optimizing the use of available data to enhance diagnostic accuracy. It utilizes a blend of convolutional and transformer-based components to capture the nuanced interplay of local and global tissue features, thus ensuring robust performance without the necessity for costly feature generation processes traditionally used to compensate for missing modalities. Our extensive testing shows MultiStainKD’s superior capability in managing missing modalities and setting new benchmarks in AD and CTE prediction accuracy. By leveraging the inherent complementary information within accessible stains and slides, MultiStainKD demonstrates its practical value and effectiveness, aligning with the goal of maintaining diagnostic quality despite the unavailability of comprehensive staining modalities. This advancement signifies an important step forward in multimodal learning for digital pathology, potentially enhancing diagnostic accuracy and supporting patient care when full modality data is not available.
Description
2024
License
Attribution 4.0 International