[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-383728-105":59,"doc-detail-383728-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","improving-the-quantitative-analysis-of-breast-microcalcifications-a-multiscale-approach","Improving the Quantitative Analysis of Breast Microcalcifications - A Multiscale Approach","","Accurate characterization of microcalcifications (MCs) in 2D digital mammography supports reducing diagnostic uncertainty for indeterminate findings. Quantitative MC analysis can improve identification of cases more likely to correspond to ductal carcinoma in situ or invasive cancer, yet automated MC detection and segmentation remain difficult due to high false positives. This work introduces a two-stage multiscale segmentation method, followed by feature modeling to classify benign versus malignant cases.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/improving-the-quantitative-analysis-of-breast-microcalcifications-a-multiscale-approach/383728/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/improving-the-quantitative-analysis-of-breast-microcalcifications-a-multiscale-approach/383728.png","ImageObject",300,407,{"name":92,"@type":93},"Rhys","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-26","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is quantitative analysis of breast microcalcifications important?","Question",{"text":112,"@type":113},"It helps reduce diagnostic uncertainty for indeterminate microcalcification findings and can better identify cases with higher likelihood of DCIS or invasive cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What challenges limit automated microcalcification segmentation in 2D mammography?",{"text":117,"@type":113},"Automated identification and segmentation are difficult because current approaches can produce high false positive rates.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed two-stage multiscale approach work?",{"text":121,"@type":113},"Candidate objects are first delineated using blob detection and Hessian analysis, then a regression convolutional network selects objects likely to be true microcalcifications. The extracted features are subsequently modeled for benign/malignant classification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},383728,1790459296,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},687207024643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach  \nChrysostomos Marasinou1 · Bo Li2 · Jeremy Paige2 · Akinyinka Omigbodun1 · Noor Nakhaei3 · Anne Hoyt2 · William Hsu1  \nReceived: 23 November 2021 / Revised: 4 December 2022 / Accepted: 6 December 2022 / Published online: 23 February 2023 © The Author(s) 2023  \nAbstract  \nAccurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.  \nKeywords Breast cancer · Full-field digital mammography · Microcalcifications · Segmentation  \nIntroduction  \nBreast cancer is the most common cancer in women, accounting for 12% of cancer cases worldwide [1] . Studies have shown that early detection using mammography reduces breast cancer mortality [2] . In many countries, screening programs have been established with sensitivity levels ranging between 80 and 95%[3, 4] . However, screening also results in false positive outcomes, leading to  \n* William Hsu[whsu@mednet.ucla.edu](whsu@mednet.ucla.edu)  \n1 Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Ste 420, Los Angeles 90024, USA  \n2 Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles 90095, CA, USA  \n3 Department of Computer Science, UCLA Samueli School of Engineering, Los Angeles 90095, CA, USA  \npatient anxiety, unnecessary biopsies, and the identification of clinically insignificant cancers, raising concerns about overdetection.  \nMicrocalcifications (MCs), which are small calcium deposits, are common mammographic findings where they typically appear as high optical density structures. Nearly 50% of the biopsied MCs are associated with ductal carcinoma in situ (DCIS) [5], an early form of cancer but anonobligate precursor to invasive cancer [6 , 7] . MCs are reported by radiologists using a set of qualitative descriptors related to morphology (shape) and distribution, as defined by the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) using a combination of full-field digital mammograms (FFDMs) and magnification views. Descriptors correspond to varying levels of suspicion for cancer. For example, amorphous MCs are assigned a moderate suspicion level (i.e., BI-RADS 4B) with a positive  \npredictive value (PPV3) 1 of 21%[8] . However, the assigned level of suspicion can be open to interpretation and varies by radiologists due","cbCaidMA3yNli22D","https://ap.wps.com/l/cbCaidMA3yNli22D","pdf",3595123,13,"English","# Abstract\n# Introduction","[{\"question\":\"Why is quantitative analysis of breast microcalcifications important?\",\"answer\":\"It helps reduce diagnostic uncertainty for indeterminate microcalcification findings and can better identify cases with higher likelihood of DCIS or invasive cancer.\"},{\"question\":\"What challenges limit automated microcalcification segmentation in 2D mammography?\",\"answer\":\"Automated identification and segmentation are difficult because current approaches can produce high false positive rates.\"},{\"question\":\"How does the proposed two-stage multiscale approach work?\",\"answer\":\"Candidate objects are first delineated using blob detection and Hessian analysis, then a regression convolutional network selects objects likely to be true microcalcifications. The extracted features are subsequently modeled for benign/malignant classification.\"}]","Improving the Quantitative Analysis of Breast Microcalcifications - A Multiscale Approach | PDF",1790258928,33]