{"entity": "researcher", "timestamp": "2026-05-19T06:54:01.891Z", "family": "Sabirsh", "given": "Alan", "initials": "A", "orcid": "0000-0001-5310-0281", "affiliations": ["Advanced Drug Delivery, Pharmaceutical Sciences, R&D, Astrazeneca, M\u00f6lndal, Sweden"], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/0000d5f644194d6f8daf8b7f5dd0540c.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/0000d5f644194d6f8daf8b7f5dd0540c"}}, "publications": [{"entity": "publication", "iuid": "6f1b382028f74b88ac1be4c6c2cb017d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d"}}, "title": "SimSearch: A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images.", "authors": [{"family": "Gupta", "given": "Ankit", "initials": "A", "orcid": "0000-0002-9961-1041", "researcher": {"href": "https://publications.scilifelab.se/researcher/ef29c8c1354a4c6d9ca8b0edaa034710.json"}}, {"family": "Sabirsh", "given": "Alan", "initials": "A", "orcid": "0000-0001-5310-0281", "researcher": {"href": "https://publications.scilifelab.se/researcher/0000d5f644194d6f8daf8b7f5dd0540c.json"}}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "issn-l": null, "volume": "26", "issue": "8", "pages": "4079-4089"}, "abstract": "Large-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments.\n\nThe user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches.\n\nThe framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction.\n\nSimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction.\n\nSimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.", "doi": "10.1109/JBHI.2022.3177602", "pmid": "35609108", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2022-11-02T06:18:25.917Z", "modified": "2023-06-19T12:58:45.375Z"}]}