71: Enhancing computer vision analysis of biopharmaceutical imaging with foundation model fine-tuning

Thomas M. Menino Convention & Exhibition Center 

Pharmaceutical research and development often uses imaging of formulations as an essential component of their pipelines for small-molecule, biologic, and vaccine development. These imaging techniques include cryo-electron microscopy (cryoEM) for high-resolution imaging and micro-flow imaging (MFI) for analysis of sub-visible particles, among others. Deep learning approaches can provide automated, accurate and scalable analytics by performing a wide range of tasks, including object detection, segmentation, and classification. Benefits of quantitative imaging include enhanced quality control and improved scientific risk identification and mitigation. However, models often require modality and task-specific development and training. Foundation models for computer vision have emerged as promising tools to increase learning efficiency for a wide range of tasks by leveraging pre-training on large datasets. We show that using foundation models can result in improved performance in cryoEM segmentation and MFI particle classification, indicating that these models provide a flexible framework for generating insights from multiple imaging modalities to aid pharmaceutical development.

biopharmaceutical

computer vision

imaging 

Section on Statistics in Imaging