FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2050978844> ?p ?o ?g. }

• W2050978844 endingPage "629" @default.
• W2050978844 startingPage "617" @default.
• W2050978844 abstract "In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate both priors into our algorithm and rigorously evaluate system performance, extracting statistics from over 6000 simulations run across 40 RP specimens. Perhaps the most indicative result is as follows: at a CaP sensitivity of 0.87 the accompanying false positive rates of the system when alternately employing the PPMM and Potts priors are 0.10 and 0.20, respectively." @default.
• W2050978844 created "2016-06-24" @default.
• W2050978844 creator A5004313839 @default.
• W2050978844 creator A5009230144 @default.
• W2050978844 creator A5023095072 @default.
• W2050978844 creator A5027642699 @default.
• W2050978844 creator A5027779880 @default.
• W2050978844 creator A5028140714 @default.
• W2050978844 creator A5039803568 @default.
• W2050978844 creator A5051527497 @default.
• W2050978844 creator A5082167522 @default.
• W2050978844 creator A5086084312 @default.
• W2050978844 date "2010-08-01" @default.
• W2050978844 modified "2023-10-16" @default.
• W2050978844 title "High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models" @default.
• W2050978844 cites W109881820 @default.
• W2050978844 cites W1494743230 @default.
• W2050978844 cites W1554544485 @default.
• W2050978844 cites W1603593764 @default.
• W2050978844 cites W1604521381 @default.
• W2050978844 cites W1667165204 @default.
• W2050978844 cites W1819910556 @default.
• W2050978844 cites W1833167738 @default.
• W2050978844 cites W1997063559 @default.
• W2050978844 cites W2000358470 @default.
• W2050978844 cites W2016456440 @default.
• W2050978844 cites W2019635781 @default.
• W2050978844 cites W2021640751 @default.
• W2050978844 cites W2029673502 @default.
• W2050978844 cites W2032704701 @default.
• W2050978844 cites W2036488048 @default.
• W2050978844 cites W2058792133 @default.
• W2050978844 cites W2062548587 @default.
• W2050978844 cites W2083043726 @default.
• W2050978844 cites W2089580584 @default.
• W2050978844 cites W2092515684 @default.
• W2050978844 cites W2092946690 @default.
• W2050978844 cites W2098979973 @default.
• W2050978844 cites W2099835437 @default.
• W2050978844 cites W2100115174 @default.
• W2050978844 cites W2104870101 @default.
• W2050978844 cites W2107884096 @default.
• W2050978844 cites W2108584285 @default.
• W2050978844 cites W2109269939 @default.
• W2050978844 cites W2114220616 @default.
• W2050978844 cites W2114502745 @default.
• W2050978844 cites W2115384210 @default.
• W2050978844 cites W2120605054 @default.
• W2050978844 cites W2123230901 @default.
• W2050978844 cites W2124014596 @default.
• W2050978844 cites W2125878029 @default.
• W2050978844 cites W2136573752 @default.
• W2050978844 cites W2139225412 @default.
• W2050978844 cites W2140368829 @default.
• W2050978844 cites W2140789035 @default.
• W2050978844 cites W2143093135 @default.
• W2050978844 cites W2146655125 @default.
• W2050978844 cites W2149696900 @default.
• W2050978844 cites W2156398782 @default.
• W2050978844 cites W2160633263 @default.
• W2050978844 cites W2160738726 @default.
• W2050978844 cites W2164710647 @default.
• W2050978844 cites W2169099300 @default.
• W2050978844 cites W221956105 @default.
• W2050978844 cites W2799061466 @default.
• W2050978844 cites W85976583 @default.
• W2050978844 cites W2479461533 @default.
• W2050978844 doi "https://doi.org/10.1016/j.media.2010.04.007" @default.
• W2050978844 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2916937" @default.
• W2050978844 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20493759" @default.
• W2050978844 hasPublicationYear "2010" @default.
• W2050978844 type Work @default.
• W2050978844 sameAs 2050978844 @default.
• W2050978844 citedByCount "111" @default.
• W2050978844 countsByYear W20509788442012 @default.
• W2050978844 countsByYear W20509788442013 @default.
• W2050978844 countsByYear W20509788442014 @default.
• W2050978844 countsByYear W20509788442015 @default.
• W2050978844 countsByYear W20509788442016 @default.
• W2050978844 countsByYear W20509788442017 @default.
• W2050978844 countsByYear W20509788442018 @default.
• W2050978844 countsByYear W20509788442019 @default.
• W2050978844 countsByYear W20509788442020 @default.
• W2050978844 countsByYear W20509788442021 @default.
• W2050978844 countsByYear W20509788442022 @default.
• W2050978844 countsByYear W20509788442023 @default.
• W2050978844 crossrefType "journal-article" @default.
• W2050978844 hasAuthorship W2050978844A5004313839 @default.
• W2050978844 hasAuthorship W2050978844A5009230144 @default.
• W2050978844 hasAuthorship W2050978844A5023095072 @default.
• W2050978844 hasAuthorship W2050978844A5027642699 @default.
• W2050978844 hasAuthorship W2050978844A5027779880 @default.
• W2050978844 hasAuthorship W2050978844A5028140714 @default.
• W2050978844 hasAuthorship W2050978844A5039803568 @default.
• W2050978844 hasAuthorship W2050978844A5051527497 @default.
• W2050978844 hasAuthorship W2050978844A5082167522 @default.
• W2050978844 hasAuthorship W2050978844A5086084312 @default.
• W2050978844 hasBestOaLocation W20509788442 @default.

• next