{"entity": "publication", "iuid": "7fb8bab5a9d343acad951f24e2ee73da", "timestamp": "2026-08-22T06:55:31.737Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7fb8bab5a9d343acad951f24e2ee73da.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7fb8bab5a9d343acad951f24e2ee73da"}}, "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"}, {"family": "Sabirsh", "given": "Alan", "initials": "A"}, {"family": "Wahlby", "given": "Carolina", "initials": "C"}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM"}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "volume": "26", "issue": "8", "pages": "4079-4089", "issn-l": null}, "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": [], "xrefs": [], "notes": [], "created": "2026-08-20T11:16:39.472Z", "modified": "2026-08-21T09:29:42.987Z"}