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Deep learning and c...
Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
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- Wieslander, Håkan (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab
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- Harrison, Philip J. (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
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- Skogberg, Gabriel (författare)
- Department COPD and IPF, Respiratory, Inflammation and Autoimmunity, R&D, AstraZeneca, Gothenburg, Sweden
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- Jackson, Sonya (författare)
- Department of Translational Science and Experimental Medicine, Respiratory, Inflammation and Autoimmunity, R&D, AstraZeneca, Gothenburg, Sweden
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- Fridén, Markus (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Department of Drug Metabolism and Pharmacokinetics, Respiratory, Inflammation and Autoimmunity, R&D, AstraZeneca, Gothenburg, Sweden,Translationell PKPD
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- Karlsson, Johan (författare)
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, Astra Zeneca, Gothenburg, Sweden
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- Spjuth, Ola, Professor, 1977- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
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- Wählby, Carolina, professor, 1974- (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Science for Life Laboratory, SciLifeLab
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2021
- 2021
- 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. ; 25:2, s. 371-380
- 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://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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 subregions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at high 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 high 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 high confidence reduces noise and increases separability of observed biological effects.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Conformal prediction
- deep learning
- digital pathology
- hierarchical analysis
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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