By Anders Tingberg, Kristina Lång, Pontus Timberg
This e-book constitutes the refereed lawsuits of the thirteenth foreign Workshop on Breast Imaging, IWDM 2016, held in Malmö, Sweden, in June 2016.
The 35 revised complete papers and 50 revised poster papers offered including 6 invited talks have been rigorously reviewed and chosen from 89 submissions. The papers are geared up in topical sections on screening; CAD; mammography, tomosynthesis, and breast CT; novel expertise; density overview and tissue research; dose and category; photograph processing, CAD, breast density, and new know-how; contrast-enhanced imaging; part distinction breast imaging; simulations and digital scientific trials.
Read Online or Download Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings PDF
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Extra resources for Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
This approach uses all information within the patch that is supplied to the CNN and determines its most descriptive features between the two groups by itself. Training of the CNN is an iterative process, where in each iteration (or epoch) network parameters and discriminative parameters are optimized. In each epoch, the CNN minimizes a cost function by updating its parameters via back-propagation. A CNN consist of several layers, the most commonly used layers are the convolutional layers, the pooling layers and the fully connected layers.
3 cm. Measurements of Half-Value Layer and entrance exposure made in 2D (FFDM) mode for the different technique factors selected by the unit when imaging the SDNR phantom for AEC performance are used. The dose is calculated as per Dance et al. , using a “t-factor” to adjust for the difference between tomosynthesis geometry and the conventional FFDM measurement conditions. These calculated doses are then compared with the estimate of dose in the DICOM header. AEC Performance. The performance of the AEC was assessed by imaging the modular phantom with the SDNR insert in the conﬁgurations given in Table 4, with foam spacers added to achieve compression paddle heights such that the attenuation of the PMMA matches the attenuation of breasts of different compressed thickness, as per the work by Dance et al.
0 To determine if LUT-QNE provided statistically signiﬁcantly better microcalciﬁcation performance, we applied the bootstrap procedure. The test set was sampled with replacement 2,000 times so that each new set of sampled data contained the same number of examples as the original set. For each FPR range and detector considered, the diﬀerence ΔPAUC in PAUC between the detector with and without LUT-QNE was computed. Resampling 2,000 times resulted in 2,000 values for ΔPAUC. p-values were deﬁned as the fraction of ΔPAUC values that were negative or zero, corresponding to cases in which LUT-QNE Look-Up-Table Quantum Noise Equalization in Digital Mammograms (a) 33 (b) Fig.