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 offered including 6 invited talks have been rigorously reviewed and chosen from 89 submissions. The papers are equipped in topical sections on screening; CAD; mammography, tomosynthesis, and breast CT; novel expertise; density overview and tissue research; dose and class; snapshot processing, CAD, breast density, and new know-how; contrast-enhanced imaging; section 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 info for Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
Assessing classiﬁers in terms of the partial area under the ROC curve. Comput. Stat. Data Anal. 64, 51–70 (2013) 12. : Learning from unbalanced data: a cascade-based approach for detecting clustered microcalciﬁcations. Med. Image Anal. nl 2 Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy Abstract. Convolutional neural networks (CNNs) have shown to be powerful for classiﬁcation of image data and are increasingly used in medical image analysis.
On the comparison of FROC curves in mammography CAD systems. Med. Phys. 32, 412–417 (2005) 2. : Learning from unbalanced data: a cascade-based approach for detecting clustered microcalciﬁcations. Med. Image Anal. 18, 241–252 (2013) 3. : Mitosis detection in breast cancer histology images with deep neural networks. , Navab, N. ) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013) 4. : A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection.
Our method can be easily and directly embedded in any existing automatic microcalciﬁcation detection scheme to boost detection performance without additional computational load. We show this for two microcalciﬁcation detection methods, namely the Haar-like feature based cascade of boosting classiﬁers (Cascade)  and the weighted Diﬀerence-of-Gaussian (DoG) ﬁlter . 1 Method Quasi-Uniform Square-Root Noise Model Quantum noise is caused by ﬂuctuations in photon ﬂuence at the detector. These ﬂuctuations can be described by a Poisson distribution and the standard deviphotons ﬂuctuate around the mean ation σq in which the number of detected √ detected number of photons λ is equal to λ .
Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings by Anders Tingberg, Kristina Lång, Pontus Timberg