{"entity": "journal", "iuid": "7eef5c72a6c24641b9a33a23e08803a4", "timestamp": "2026-08-22T06:57:00.860Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/IEEE%20J%20Biomed%20Health%20Inform.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/IEEE%20J%20Biomed%20Health%20Inform"}}, "title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "issn-l": null, "publications_count": 3, "publications": [{"entity": "publication", "iuid": "7fb8bab5a9d343acad951f24e2ee73da", "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"}, {"entity": "publication", "iuid": "5b0ad993705d44cf92833e4f01c9cbf7", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5b0ad993705d44cf92833e4f01c9cbf7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5b0ad993705d44cf92833e4f01c9cbf7"}}, "title": "Towards Automatic Protein Co-Expression Quantification in Immunohistochemical TMA Slides.", "authors": [{"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Pereira", "given": "Carla", "initials": "C"}, {"family": "Martins", "given": "Diana", "initials": "D"}, {"family": "Almeida", "given": "Raquel", "initials": "R"}, {"family": "Carneiro", "given": "Fatima", "initials": "F"}, {"family": "Almeida", "given": "Gabriela M", "initials": "GM"}, {"family": "Oliveira", "given": "Carla", "initials": "C"}, {"family": "Wahlby", "given": "Carolina", "initials": "C"}], "type": "journal article", "published": "2021-02-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "volume": "25", "issue": "2", "pages": "393-402", "issn-l": null}, "abstract": "Immunohistochemical (IHC) analysis of tissue biopsies is currently used for clinical screening of solid cancers to assess protein expression. The large amount of image data produced from these tissue samples requires specialized computational pathology methods to perform integrative analysis. Even though proteins are traditionally studied independently, the study of protein co-expression may offer new insights towards patients' clinical and therapeutic decisions. To explore protein co-expression, we constructed a modular image analysis pipeline to spatially align tissue microarray (TMA) image slides, evaluate alignment quality, define tumor regions, and ultimately quantify protein expression, before and after tumor segmentation. The pipeline was built with open-source tools that can manage gigapixel slides. To evaluate the consensus between pathologist and computer, we characterized a cohort of 142 gastric cancer (GC) cases regarding the extent of E-cadherin and CD44v6 expression. We performed IHC analysis in consecutive TMA slides and compared the automated quantification with the pathologists' manual assessment. Our results show that automated quantification within tumor regions improves agreement with the pathologists' classification. A co-expression map was created to identify the cores co-expressing both proteins. The proposed pipeline provides not only computational tools forwarding current pathology practices to explore co-expression, but also a framework for merging data and transferring information in learning-based approaches to pathology.", "doi": "10.1109/JBHI.2020.3008821", "pmid": "32750958", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T11:16:37.827Z", "modified": "2026-08-21T09:29:40.936Z"}, {"entity": "publication", "iuid": "b235273eec86487bbec87ce83f0f51fc", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b235273eec86487bbec87ce83f0f51fc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b235273eec86487bbec87ce83f0f51fc"}}, "title": "Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images.", "authors": [{"family": "Wieslander", "given": "Hakan", "initials": "H"}, {"family": "Harrison", "given": "Philip J", "initials": "PJ"}, {"family": "Skogberg", "given": "Gabriel", "initials": "G"}, {"family": "Jackson", "given": "Sonya", "initials": "S"}, {"family": "Friden", "given": "Markus", "initials": "M"}, {"family": "Karlsson", "given": "Johan", "initials": "J"}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Wahlby", "given": "Carolina", "initials": "C"}], "type": "journal article", "published": "2021-02-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "volume": "25", "issue": "2", "pages": "371-380", "issn-l": null}, "abstract": "With the increasing amount of image data collected from biomedical experiments there is an urgent need for smarter and more effective analysis methods. Many scientific questions require analysis of image sub-regions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at full resolution can reduce computational requirements and save time. In this paper we propose a three-step pipeline: First, bounding boxes for ROIs are located at low resolution. Next, ROIs are subjected to semantic segmentation into sub-regions at mid-resolution. We also estimate the confidence of the segmented sub-regions. Finally, quantitative measurements are extracted at full resolution. We use deep learning for the first two steps in the pipeline and conformal prediction for confidence assessment. We show that limiting final quantitative analysis to sub-regions with full confidence reduces noise and increases separability of observed biological effects.", "doi": "10.1109/JBHI.2020.2996300", "pmid": "32750907", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T11:16:35.961Z", "modified": "2026-08-21T09:29:39.048Z"}], "created": "2026-08-20T11:16:35.981Z", "modified": "2026-08-20T11:16:35.981Z"}