DEPICTER: Deep representation clustering for histology annotation

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Chelebian, EduardUppsala universitet,Institutionen för informationsteknologi,Science for Life Laboratory, SciLifeLab (author)

DEPICTER : Deep representation clustering for histology annotation

Article/chapterEnglish2024

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Elsevier,2024
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LIBRIS-ID:oai:DiVA.org:uu-528262
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-528262URI
https://doi.org/10.1016/j.compbiomed.2024.108026DOI

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Language:English
Summary in:English

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Automatic segmentation of histopathology whole -slide images (WSI) usually involves supervised training of deep learning models with pixel -level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully supervised segmentation requires large-scale data manually annotated by experts, which can be expensive and time-consuming to obtain. Non -fully supervised methods, ranging from semi -supervised to unsupervised, have been proposed to address this issue and have been successful in WSI segmentation tasks. But these methods have mainly been focused on technical advancements in algorithmic performance rather than on the development of practical tools that could be used by pathologists or researchers in real -world scenarios. In contrast, we present DEPICTER (Deep rEPresentatIon ClusTERing), an interactive segmentation tool for histopathology annotation that produces a patch -wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi -supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi -resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating. We report both real-time interaction results with three pathologists and evaluate the performance on three public cancer classification dataset benchmarks through simulations. The code and demos of DEPICTER are publicly available at https://github.com/eduardchelebian/depicter.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
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
Interactive annotation
Histology
Self-supervised learning
Clustering

Added entries (persons, corporate bodies, meetings, titles ...)

Avenel, ChristopheUppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab(Swepub:uu)chrav452 (author)
Ciompi, FrancescoRadboud Univ Nijmegen, Dept Pathol, Nijmegen Med Ctr, Nijmegen, Netherlands. (author)
Wählby, Carolina,professor,1974-Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab(Swepub:uu)cli05194 (author)
Uppsala universitetInstitutionen för informationsteknologi (creator_code:org_t)

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In:Computers in Biology and Medicine: Elsevier1700010-48251879-0534

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