Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis

Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses. The proposed network is designed to encode mass characteristics in two parts. First, 2D spatial image characteristics of DBT slices are encoded as a slice feature representation by convolutional neural network (CNN). Then, depth directional characteristics of masses among the slice feature representations are encoded by the proposed depth directional long-term recurrent learning. In addition, to further improve the class discriminability of latent feature representation, we have devised three objective functions aiming to (a) minimize classification error, (b) minimize intra-class variation within the same class, and (c) preserve feature representation consistency in a central slice. Experimental results have demonstrated that the proposed latent feature representation achieves a higher level of classification performance in terms of receiver operating characteristic (ROC) curves and the area under the ROC curve values compared to performance with feature representation learned by conventional CNN and hand-crafted features.

[1]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[2]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[5]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[6]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[7]  Yong Man Ro,et al.  Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection. , 2015, Physics in medicine and biology.

[8]  Berkman Sahiner,et al.  Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience. , 2005, Radiology.

[9]  Tao Wu,et al.  A comparison of reconstruction algorithms for breast tomosynthesis. , 2004, Medical physics.

[10]  Berkman Sahiner,et al.  Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches. , 2008, Medical physics.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[14]  Yong Man Ro,et al.  Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification , 2014, Physics in medicine and biology.

[15]  Martin D. Fox,et al.  Classifying mammographic lesions using computerized image analysis , 1993, IEEE Trans. Medical Imaging.

[16]  Frédéric Jurie,et al.  PCCA: A new approach for distance learning from sparse pairwise constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[18]  Mohamed Cheriet,et al.  Feature Design for Offline Arabic Handwriting Recognition: Handcrafted vs Automated? , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[19]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[20]  Nico Karssemeijer,et al.  Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. , 2013, Medical physics.

[21]  Nico Karssemeijer,et al.  Generating Synthetic Mammograms From Reconstructed Tomosynthesis Volumes , 2013, IEEE Transactions on Medical Imaging.

[22]  Charles E Metz,et al.  Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. , 2006, Journal of the American College of Radiology : JACR.

[23]  M. Giger,et al.  Computerized mass detection for digital breast tomosynthesis directly from the projection images. , 2006, Medical physics.

[24]  Bo Zhao,et al.  Image artifacts in digital breast tomosynthesis: investigation of the effects of system geometry and reconstruction parameters using a linear system approach. , 2008, Medical physics.

[25]  Hyo-Eun Kim,et al.  A novel approach for tuberculosis screening based on deep convolutional neural networks , 2016, SPIE Medical Imaging.

[26]  T. Freer,et al.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.

[27]  Sukhendu Das,et al.  A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification , 2010, IETE Technical Review.

[28]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[29]  Lubomir M. Hadjiiski,et al.  A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. , 2006, Medical physics.

[30]  Yong Man Ro,et al.  Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes , 2014, Physics in medicine and biology.

[31]  J. Baker,et al.  Breast tomosynthesis: state-of-the-art and review of the literature. , 2011, Academic radiology.

[32]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[33]  Nico Karssemeijer,et al.  Optimizing Case-Based Detection Performance in a Multiview CAD System for Mammography , 2011, IEEE Transactions on Medical Imaging.

[34]  Hermann Ney,et al.  Cross-entropy vs. squared error training: a theoretical and experimental comparison , 2013, INTERSPEECH.

[35]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[36]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[37]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[38]  Ehsan Samei,et al.  Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach. , 2008, Medical physics.

[39]  J M Lesniak,et al.  Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography , 2012, Physics in medicine and biology.

[40]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[41]  Yong Man Ro,et al.  Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms , 2012, Physics in medicine and biology.

[42]  C P Lawinski,et al.  A comparison of the accuracy of film-screen mammography, full-field digital mammography, and digital breast tomosynthesis. , 2012, Clinical radiology.

[43]  Isabelle Bloch,et al.  Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches , 2014, Pattern Recognit..