Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net.
The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may im...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1238 - 1250 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159758940&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758940 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2022 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159758940 156616606 159758940 159758940 10.1007/s10278-022-00641-8 159758940 ppf: 1238 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net. aug: au: Nofallah, Shima Mokhtari, Mojgan Wu, Wenjun Mehta, Sachin Knezevich, Stevan May, Caitlin J. Chang, Oliver H. Lee, Annie C. Elmore, Joann G. Shapiro, Linda G. affil: University of Washington, 98195, Seattle, WA, USA sug: subj: Biopsy Methods Melanoma Diagnosis Deep Learning Melanoma Prognosis Microscopy, Virtual Image Processing, Computer Assisted Methods Human Semantics Public Health Epidermis Dermis Magnetic Resonance Imaging ab: The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. The histologic evaluation of melanocytic lesions, including melanoma and its precursors, involves determining whether the melanocytic population involves the epidermis, dermis, or both. Semantic segmentation of clinically important structures in skin biopsies is a crucial step towards an accurate diagnosis. While training a segmentation model requires ground-truth labels, annotation of large images is a labor-intensive task. This issue becomes especially pronounced in a medical image dataset in which expert annotation is the gold standard. In this paper, we propose a two-stage segmentation pipeline using coarse and sparse annotations on a small region of the whole slide image as the training set. Segmentation results on whole slide images show promising performance for the proposed pipeline. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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