Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images

Chemical staining methods are dependable but require extensive time, expensive chemicals, and raise environmental concerns. These challenges highlight the need for alternative solutions like virtual staining, which accelerates the diagnostic process and enhances stain application flexibility. Generative AI technologies are pivotal in addressing these issues. However, the high-stakes nature of healthcare decisions, especially in computational pathology, complicates the adoption of these tools due to their opaque processes. Our work introduces the use of generative AI for virtual staining, aiming to enhance performance, trustworthiness, scalability, and adaptability in computational pathology. The methodology centers on a singular H&E encoder supporting multiple stain decoders. This design focuses on critical regions in the latent space of H&E, enabling precise synthetic stain generation. Our method, tested to generate 8 different stains from a single H&E slide, offers scalability by loading only necessary model components during production. We integrate label-free knowledge in training, using loss functions and regularization to minimize artifacts, thus improving paired/unpaired virtual staining accuracy. To build trust, we use real-time self-inspection with discriminators for each stain type, providing pathologists with confidence heat-maps. Automatic quality checks on new H&E slides ensure conformity to the trained distribution, ensuring accurate synthetic stains. Recognizing pathologists' challenges with new technologies, we have developed an open-source, cloud-based system, that allows easy virtual staining of H&E slides through a browser, addressing hardware/software issues and facilitating real-time user feedback. We also curated a novel dataset of 8 paired H&E/stains related to pediatric Crohn's disease, comprising 480 WSIs to further stimulate computational pathology research.

Phagocytosis Unveiled: A Scalable and Interpretable Deep learning Framework for Neurodegenerative Disease Analysis

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from backgrounds make this task challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce a fully automated, scalable, and versatile realtime framework for quantifying and analyzing phagocytic activity. Our proposed pipeline can process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, but rather provides essential deep learning algorithm optimization insights and solutions. Incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. To stimulate translational approaches and future research, we release an open-source pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to this critical domain. https://github.com/ounissimehdi/PhagoStat