BibSLEIGH corpus
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Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
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Used together with:
detect (5)
cluster (4)
mammogram (4)
base (3)
featur (3)

Stem microcalcif$ (all stems)

7 papers:

ICPRICPR-2008-ChangFLI #clustering #detection #kernel #multi
Clustered Microcalcification detection based on a Multiple Kernel Support Vector Machine with Grouped Features (GF-SVM) (TTC, JF, HWL, HHSI), pp. 1–4.
ICPRICPR-2008-WuJP #detection #effectiveness #linear
Effective features based on normal linear structures for detecting microcalcifications in mammograms (ZQW, JJ, YP), pp. 1–4.
ICPRICPR-v3-2004-DEliaMMPST #classification #clustering #detection #segmentation
Detection of Microcalcifications Clusters in Mammograms through TS-MRF Segmentation and SVM-based Classification (CD, CM, MM, GP, GS, FT), pp. 742–745.
ICPRICPR-v1-2000-GulsrudH #clustering #detection
Optimal Filter for Detection of Clustered Microcalcifications (TOG, JHH), pp. 1508–1511.
ICPRICPR-v1-2000-Rodriguez-SanchezGFF #how
How to Define the Notion of Microcalcifications in Digitized Mammograms (RRS, JAG, JFV, XRFV), pp. 1494–1499.
ICPRICPR-v4-2000-CordellaTV #clustering
Combining Experts with Different Features for Classifying Clustered Microcalcifications in Mammograms (LPC, FT, MV), pp. 4324–4327.
ICPRICPR-v4-2000-MataNS #detection #multi #using
Microcalcifications Detection Using Multiresolution Methods (RM, EN, FS), pp. 4344–4347.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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