Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.

[1]  W. Kaiser,et al.  The Necrosis Sign in Magnetic Resonance‐Mammography: Diagnostic Accuracy in 1,084 Histologically Verified Breast Lesions , 2010, The breast journal.

[2]  W. Kaiser,et al.  Sensitivity and Specificity of Unilateral Edema on T2w‐TSE Sequences in MR‐Mammography Considering 974 Histologically Verified Lesions , 2010, The breast journal.

[3]  W. Kaiser,et al.  The Adjacent Vessel Sign on Breast MRI: New Data and a Subgroup Analysis for 1,084 Histologically Verified Cases , 2010, Korean journal of radiology.

[4]  W. Kaiser,et al.  The hook sign for differential diagnosis of malignant from benign lesions in magnetic resonance mammography: Experience in a study of 1084 histologically verified cases , 2010, Acta radiologica.

[5]  E. Setti,et al.  A use of a neural network to evaluate contrast enhancement curves in breast magnetic resonance images , 2001, Journal of Digital Imaging.

[6]  Min-Ying Su,et al.  Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. , 2009, Academic radiology.

[7]  F. Gilbert,et al.  Evidence-based radiology: why and how? , 2009, European Radiology.

[8]  Francesco Sardanelli,et al.  Biostatistics for Radiologists: Planning, Performing, and Writing a Radiologic Study , 2008 .

[9]  Li Lan,et al.  Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. , 2008, Academic radiology.

[10]  Les Irwig,et al.  Accuracy and surgical impact of magnetic resonance imaging in breast cancer staging: systematic review and meta-analysis in detection of multifocal and multicentric cancer. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Wendy B DeMartini,et al.  A Review of Current Evidence-Based Clinical Applications for Breast Magnetic Resonance Imaging , 2008, Topics in magnetic resonance imaging : TMRI.

[12]  Werner Alois Kaiser,et al.  Signs in MR-Mammography , 2007 .

[13]  C. Gatsonis,et al.  MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. , 2007, The New England journal of medicine.

[14]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[15]  N. Hylton,et al.  Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. , 2006, Radiology.

[16]  Joachim Boettcher,et al.  Further Signs in the Evaluation of Magnetic Resonance Mammography: A Retrospective Study , 2005, Investigative radiology.

[17]  Peter Aspelin,et al.  Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast , 2004, European Radiology.

[18]  W. A. Kaiser,et al.  Is the “blooming sign” a promising additional tool to determine malignancy in MR mammography? , 2004, European Radiology.

[19]  M Thelen,et al.  Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography. , 2003, Medical physics.

[20]  Berkman Sahiner,et al.  Optimal neural network architecture selection: improvement in computerized detection of microcalcifications. , 2002, Academic radiology.

[21]  M D Schnall,et al.  Update of breast MR imaging architectural interpretation model. , 2001, Radiology.

[22]  G Brix,et al.  Classification of signal-time curves from dynamic MR mammography by neural networks. , 2001, Magnetic resonance imaging.

[23]  M L Giger,et al.  Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. , 2000, Academic radiology.

[24]  E Grabbe,et al.  Breast carcinoma: effect of preoperative contrast-enhanced MR imaging on the therapeutic approach. , 1999, Radiology.

[25]  Terrence L. Fine,et al.  Feedforward Neural Network Methodology , 1999, Information Science and Statistics.

[26]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[27]  P Abdolmaleki,et al.  Neural network analysis of breast cancer from MRI findings. , 1997, Radiation medicine.

[28]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[29]  Martin A. Riedmiller,et al.  Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .

[30]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[31]  Martin Bland,et al.  An Introduction to Medical Statistics , 1987 .

[32]  MRI of the female breast. First clinical results. , 1985, Archives internationales de physiologie et de biochimie.

[33]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[34]  M. Stone.  Cross-validation and multinomial prediction , 1974 .