By Anders Tingberg, Kristina Lång, Pontus Timberg
This booklet constitutes the refereed lawsuits of the thirteenth overseas Workshop on Breast Imaging, IWDM 2016, held in Malmö, Sweden, in June 2016.
The 35 revised complete papers and 50 revised poster papers awarded including 6 invited talks have been rigorously reviewed and chosen from 89 submissions. The papers are prepared in topical sections on screening; CAD; mammography, tomosynthesis, and breast CT; novel expertise; density overview and tissue research; dose and category; picture processing, CAD, breast density, and new know-how; contrast-enhanced imaging; part distinction breast imaging; simulations and digital scientific trials.
Read or Download Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings PDF
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Additional resources for Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
03) Mean glandular doses for the different phantom thicknesses imaged to characterize AEC and dose are given in Table 4. 18 A. Maki et al. Table 4. Typical mean glandular dose values as calculated from physicist’s measurements (C) and reported in the DICOM header (D). PMMA/Equiv. 2 4 Discussion The measured SDNR values are reasonably stable over time. Upgrades to the equipment resulted in marked shifts in the operating levels. The MTF measurements are very sensitive to background correction and normalization making serial monitoring difﬁcult.
The validation set is classiﬁer by the GentleBoost classier after adding a weak classiﬁer. If the criteria are not met, another weak classiﬁer is added to the GentleBoost classiﬁer. Training of the node ﬁnishes when the criteria are met or the maximum number of weak classiﬁers is reached. After each node, all samples in the training set are classiﬁed by the newly formed node and samples are removed when they received a classiﬁcation score below the trained threshold. Training of the whole cascade is stopped when there are too few negative samples left in the training set.
The noise level) β as in Eq. 5. This conﬁrms that the proposed scale transform eﬀectively equalizes the noise in a single mammogram and also theoretically validates the analytical formulation for noise level inhomogeneity σβ derived in Eq. 9. 1 Results Dataset The assessment of the proposed noise equalization method was performed on 252 raw DICOM ‘FOR PROCESSING’ digital mammograms extracted from a Look-Up-Table Quantum Noise Equalization in Digital Mammograms 31 private database containing more than 40, 000 cases obtained in routine screening.