2022/07/13 by Singh, Pranav, Jacopo Cirrone, Cirrone, Jacopo
Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Inflammatory Myopathies and Dermatomyositis #Machine Learning (cs.LG) #Systemic Lupus Erythematosus Research #Systemic Sclerosis and Related Diseases #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2207.06489
openalex publication_date 2022/07/13 · openalex created_date 2022/07/16 · openalex updated_date 2026/07/28
The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding great promise for novel insights. In this paper, we empirically develop deep learning approaches that use dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%.