Poster
in
Workshop: Medical Imaging meets NeurIPS
A multi-modal image pipeline for automated generation of large, labeled H&E image data-sets.
Matthew Lee · Victoria Fang · Rami Vanguri · Abigail Zellmer · Amy Baxter · Dokyoon Kim · Derek Oldridge · John Wherry
Abstract:
We leverage paired multiplex immunofluorescence (mpIF) imaging to identify cell types in hematoxylin and eosin (HE) stained images. By synergizing the strengths of these two imaging modalities, our pipeline enables accurate cell-type annotation in HE images. This breakthrough allows for the creation of a large, annotated HE dataset, significantly increasing the scalability of training data generation for machine learning models. This expansion of the dataset is especially crucial for training highly effective deep learning models, as it provides a wealth of more diverse, and representative samples, leading to improved performance and generalization. The pipeline’s ability to generate such a large, annotated dataset offers a valuable resource for detailed analysis and characterization of cell populations, facilitating advanced machine learning applications in pathology and biomedical imaging.
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