[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126683-en":3,"doc-seo-126683-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126683,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Machine learning, unsupervised learning and stain normalization in digital nephropathology - Master thesis 2023","The thesis investigates how stain normalization impacts unsupervised learning clustering of glomeruli in digital nephropathology for chronic kidney disease. A three-stage unsupervised pipeline is implemented to compare clustering performance when applying Reinhard stain normalization, Macenko stain normalization, and structure preserving color normalization, using different reference images. Performance is quantified with the Adjusted Rand Index to measure agreement between clustering results and true labels. Results show increased cluster agreement for all normalization methods, with Reinhard achieving the strongest overall performance.","| \u003Cbr>\u003Cbr>Faculty of Science and Technology Department of Electrical Engineering and Computer Science\u003Cbr>MASTER THESIS |  |\n| --- | --- |\n| Study line / specialization: Master in engineering / Signal processing and robotics with medical technology | Spring semester 2023\u003Cbr>Open |\n| Author: Jon Tveit |  |\n| Main supervisor Hrafn Weishaupt\u003Cbr>Local supervisor Kjersti Engan\u003Cbr>Co-supervisor: Sabine Leh |  |\n| Title for thesis: Machine learning, unsupervised learning\u003Cbr>and stain normalization in digital nephropathology |  |\n| Study points: 30 |  |\n| Subject words:\u003Cbr>Digital Pathology\u003Cbr>Stain normalization | Page numbers: 56\u003Cbr>+ appendix: 2\u003Cbr>Stavanger 15 . June 2023 |\n\nContents  \nContents i  \nSummary v  \n1 Introduction 1  \n1.1 Problem description ...................... 1  \n1.2 Aims and objectives ...................... 2  \n2 Background 4  \n2.1 Chronic kidney disease ..................... 4  \n2.1.1 The kidney biopsy and digital histopathology .... 5  \n2.1.2 Glomeruli and morphological changes ........ 5  \n2.1.3 Diagnosing CKD .................... 7  \n2.1.4 Stain variation ..................... 7  \n2.2 Machine learning ........................ 8  \ni  \n2.2.1 Supervised learning ................... 8  \n2.2.2 Unsupervised learning ................. 9  \n2.2.3 Dimensionality reduction and cluster agreement ... 11  \n2.3 Stain normalization ....................... 12  \n2.3.1 Reinhard stain normalization ............. 12  \n2.3.2 Macenko stain normalization ............. 12  \n2.3.3 Structure preserving stain normalization ....... 13  \n3 Methods 14  \n3.1 Overall approach ........................ 14  \n3.2 Data materials ......................... 15  \n3.3 Step 1: Stain normalization .................. 16  \n3.3.1 Reference images .................... 19  \n3.3.2 Reinhard stain normalization ............. 20  \n3.3.3 Macenko stain normalization ............. 21  \n3.3.4 Structure preserving colour normalization ...... 22  \n3.3.5 Masking tissue of tissue background ......... 23  \n3.3.6 Evaluating stain normalization results ........ 25  \n3.4 Step 2: Image pre-processing and feature extraction ..... 25  \n3.5 Step 3: Dimensionality reduction ............... 26  \nii  \n3.6 Step 4: Clustering ....................... 26  \n3.6.1 Evaluating clustering results .............. 27  \n4 Implementation 29  \n4.1 Available hardware ....................... 29  \n4.2 Image reading .......................... 30  \n4.3 Implementation of stain normalization techniques ...... 30  \n4.3.1 Reinhard implementation ............... 30  \n4.3.2 Macenko implementation ............... 31  \n4.3.3 SPCN implementation ................. 31  \n4.3.4 Pipeline for normalizing images ............ 31  \n4.3.5 Structural similarity index metric ........... 32  \n4.4 Loading the pre-trained CNN and extracting features .... 32  \n4.5 Clustering ............................ 33  \n4.5.1 Dimensionality reduction implementation ...... 33  \n4.5.2 Clustering implementation ............... 33  \n4.5.3 Adjusted Rand Index .................. 33  \n5 Results 34  \n5.1 No normalization ........................ 36  \n5.2 Reinhard ............................ 36  \niii  \n5.3 Macenko ............................. 38  \n5.4 SPCN .............................. 40  \n6 Discussion 43  \n7 Conclusions 45  \nBibliography 50  \nAppendix I  \nAppendix A .............................. I  \nAppendix B .............................. IV  \niv  \nSummary  \nChronic kidney disease is a serious health challenge and still, the field of study lacks awareness and funding. Improving the efficiency of diagnosing chronic disease is important. Machine learning can be used for various tasks in order to make CKD diagnosis more efficient. If the disease is discovered quickly it can be possible to reverse changes. In this project, we explore techniques that can improve clustering of glomeruli images.  \nThe current thesis evaluates the effects of applying stain normalization tonephropathological data in order to improve unsupervised learning clusterin","cbCaiqiM3VCXdxoz","https://ap.wps.com/l/cbCaiqiM3VCXdxoz","pdf",14458986,1,62,"English","en",105,"# Summary\n# Introduction\n## Problem description\n## Aims and objectives\n# Background\n## Chronic kidney disease\n## Machine learning\n## Stain normalization\n# Methods\n## Overall approach\n## Data materials\n## Step 1: Stain normalization\n## Step 2: Image pre-processing and feature extraction\n## Step 3: Dimensionality reduction\n## Step 4: Clustering\n# Implementation\n## Available hardware\n## Image reading\n## Implementation of stain normalization techniques\n## Loading the pre-trained CNN and extracting features\n## Clustering\n# Results\n## No normalization\n## Reinhard\n## Macenko\n## SPCN\n# Discussion\n# Conclusions\n# Bibliography\n# Appendix","[{\"question\":\"What problem does the thesis address in digital nephropathology?\",\"answer\":\"It addresses limitations in awareness and funding for chronic kidney disease and focuses on improving the efficiency of diagnosing related image patterns through better unsupervised clustering of glomeruli images.\"},{\"question\":\"Which stain normalization techniques are evaluated?\",\"answer\":\"The thesis evaluates Reinhard stain normalization, Macenko stain normalization, and structure preserving color normalization, each tested with different reference images.\"},{\"question\":\"How is clustering performance measured?\",\"answer\":\"Clustering performance is evaluated using the Adjusted Rand Index, comparing unsupervised clustering outcomes to true labels.\"},{\"question\":\"What is the main conclusion about the best method?\",\"answer\":\"All three normalization techniques increase overall cluster agreement, and Reinhard stain normalization provides the best performance with the highest single-experiment and average Adjusted Rand Index scores.\"}]","Machine learning, unsupervised learning and stain normalization in digital nephropathology - Master thesis 2023 | PDF",1785934213,156,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-unsupervised-learning-and-stain-normalization-in-digital-nephropathology-master-thesis-2023","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-unsupervised-learning-and-stain-normalization-in-digital-nephropathology-master-thesis-2023/126683/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in digital nephropathology?","Question",{"text":75,"@type":76},"It addresses limitations in awareness and funding for chronic kidney disease and focuses on improving the efficiency of diagnosing related image patterns through better unsupervised clustering of glomeruli images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which stain normalization techniques are evaluated?",{"text":80,"@type":76},"The thesis evaluates Reinhard stain normalization, Macenko stain normalization, and structure preserving color normalization, each tested with different reference images.",{"name":82,"@type":73,"acceptedAnswer":83},"How is clustering performance measured?",{"text":84,"@type":76},"Clustering performance is evaluated using the Adjusted Rand Index, comparing unsupervised clustering outcomes to true labels.",{"name":86,"@type":73,"acceptedAnswer":87},"What is the main conclusion about the best method?",{"text":88,"@type":76},"All three normalization techniques increase overall cluster agreement, and Reinhard stain normalization provides the best performance with the highest single-experiment and average Adjusted Rand Index scores.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,122,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},40,"healthcare",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},8,"Research & Report",30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]