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SimSearch :
SimSearch : A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images
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- Gupta, Ankit (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion
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- Sabirsh, Alan (författare)
- Adv Drug Delivery, Pharmaceut Sci, R&D, S-43150 Mölndal, Sweden.
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- Wählby, Carolina, professor, 1974- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen för visuell information och interaktion
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- Sintorn, Ida-Maria, 1976- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen för visuell information och interaktion,Science for Life Laboratory, SciLifeLab,Vironova AB, 11330 Gävlegatan 22, Stockholm, Sweden.
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 26:8, s. 4079-4089
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Objective: 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. Methods: The 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. Results: The 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. Conclusions: SimSearch 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. Significance: SimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Feature extraction
- Microscopy
- Prototypes
- Training
- Noise measurement
- Manuals
- Deep learning
- Human-in-the-loop
- autom- ation
- self-supervised learning
- semi-supervised learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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