{"paper":{"title":"Using Deep Convolutional Neural Networks to Circumvent Morphological Feature Specification when Classifying Subvisible Protein Aggregates from Micro-Flow Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Austin L. Daniels, Christopher P. Calderon, Theodore W. Randolph","submitted_at":"2017-09-01T04:36:45Z","abstract_excerpt":"Flow-Imaging Microscopy (FIM) is commonly used in both academia and industry to characterize subvisible particles (those $\\le 25 \\mu m$ in size) in protein therapeutics. Pharmaceutical companies are required to record vast volumes of FIM data on protein therapeutic products, but are only mandated under US FDA regulations (i.e., USP $\\big \\langle 788 \\big \\rangle$) to control the number of particles exceeding $10$ and $25 \\mu m$ in delivered products. Hence, a vast amount of digital images are available to analyze. Current state-of-the-art methods rely on a relatively low-dimensional list of \"m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00152","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}