Breast Imaging: 13th International Workshop, IWDM 2016, by Anders Tingberg, Kristina Lång, Pontus Timberg

By Anders Tingberg, Kristina Lång, Pontus Timberg

This ebook constitutes the refereed complaints 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 provided 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 class; photograph processing, CAD, breast density, and new know-how; contrast-enhanced imaging; section distinction breast imaging; simulations and digital scientific trials.

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Extra resources for Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings

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A 2 cm thick insert containing a polyethylene sphere can be added to measure how the SDNR varies with phantom thickness. Blank slabs are used to create uniform regions for evaluation of the noise power spectrum (NPS). 3 Tests The QC program includes tests to be performed daily (QC phantom) and monthly (monitor lag) by the radiographer, and semi-annually by the medical physicist (Table 1). Initial tolerances on the test results are given for the daily QC tests (Table 2). Because of limited experience with the wide variety of different technologies available, the tolerances are given with respect to the reference operating levels (ROL) established after acceptance testing of the unit.

These layers have full connections to all activations in the previous layer. This type of layers are commonly used in regular neural networks. The final layer is a fully connected layer with two output neurons, one for each class. -J. Mordang et al. The CNN structure in our study is inspired by the OxfordNet [13]. This structure consists of repetitions of two convolutional layers, with 32 filters each, followed by a max-pooling layer of size 2 × 2 and a stride of 2. Additionally, fully connected layers are used as final layers and a soft-max function calculates the final output.

The deep learning approach was compared to a current state-of-the-art method for the detection of microcalcifications: the cascade classifier. Both methods were trained on a large training set including 11,711 positive and 27 million negative samples. For testing, an independent test set was configured containing 5,298 positive and 18 million negative samples. The mammograms included in this study were acquired on mammography systems from three manufactures: Hologic, GE, and Siemens. Receiver operating characteristics analysis was carried out.

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