Roysam, Badrinath2024-01-24December 22023-12https://hdl.handle.net/10657/16151We present a versatile multiscale visual search engine for visual query-driven analysis of whole-slide multiplex IHC scans of brain tissue, without the confines, limitations, and programming needs of conventional script-based image analysis. Our unsupervised machine learning-based method adaptively learns the cytoarchitectural characteristics of provided training images, without any human effort or intervention. Then, visual queries can be submitted by indicating individual cell(s), and/or multicellular tissue patch(es) of interest, upon which the search engine retrieves a rank-ordered spatially mapped list of similar other cells or tissue patches based on similar cell morphologies, protein expression, cytoarchitecture, myeloarchitecture, vasculature, etc. Retrievals from multiple queries can be co-analyzed intuitively to generate complex inferences that would otherwise require sophisticated programming. We envision a broad range of uses, e.g., identifying cell populations, discovering cellular/cytoarchitectural similarities and differences across brain regions, delineating brain regions, fitting/refining/building atlases, delineating cortical cell layers, and proofreading automated image analysis results.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).search engine, multiplex, query-driven, deep learning, person re-id, image search, similarity search, histology, rat brain atlas2024-01-24Thesisborn digital