Paper Info
Title
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer.
Abstract
Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.
Year
DOI
Venue
2017
10.1007/978-3-319-66182-7_76
MICCAI
Field
DocType
Citations 
Normalization (image processing),Mammography,Data processing,Homogeneity (statistics),Pattern recognition,Breast cancer,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Feature learning,Machine learning
Conference
2
PageRank 
References 
Authors
0.44
4
13
Name
Order
Citations
PageRank
Paul Jaeger1121.48
Sebastian Bickelhaupt221.46
Frederik B. Laun3594.90
Wolfgang Lederer421.46
Daniel Heidi520.44
Tristan Anselm Kuder621.12
Daniel Paech771.10
David Bonekamp8171.68
Alexander Radbruch920.78
stefan delorme10225.67
Heinz-Peter Schlemmer11171.97
Franziska Steudle1221.12
Klaus H. Maier-Hein1336142.06