Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

[1]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[2]  G. Tourassi.  Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[3]  M K Markey,et al.  Application of the mutual information criterion for feature selection in computer-aided diagnosis. , 2001, Medical physics.

[4]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[5]  K. Han,et al.  Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. , 2003, Radiology.

[6]  B. van Ginneken,et al.  Computer-aided diagnosis in high resolution CT of the lungs. , 2003, Medical physics.

[7]  Ruey-Feng Chang,et al.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors , 2004, Breast Cancer Research and Treatment.

[8]  E. Hoffman,et al.  Lung image database consortium: developing a resource for the medical imaging research community. , 2004, Radiology.

[9]  Hee Chan Kim,et al.  Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features , 2004, IEEE Trans. Medical Imaging.

[10]  S. Armato,et al.  Automated lung segmentation for thoracic CT impact on computer-aided diagnosis. , 2004, Academic radiology.

[11]  Fang-Cheng Yeh,et al.  Cell-competition algorithm: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. , 2005, Ultrasound in medicine & biology.

[12]  Lubomir M. Hadjiiski,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. , 2006, Medical physics.

[13]  Chin-Shang Li,et al.  Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors. , 2006, Radiology.

[14]  Takeshi Nakaura,et al.  Pulmonary nodules: estimation of malignancy at thin-section helical CT--effect of computer-aided diagnosis on performance of radiologists. , 2006, Radiology.

[15]  André Victor Alvarenga,et al.  Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images. , 2007, Medical physics.

[16]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[17]  Lubomir M. Hadjiiski,et al.  Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. , 2007, Radiology.

[18]  W. Moon,et al.  Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[19]  M. Giger,et al.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. , 2008, Medical physics.

[20]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[21]  Berkman Sahiner,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. , 2009, Medical physics.

[22]  Hon J. Yu,et al.  Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement , 2009, European Radiology.

[23]  Maryellen L. Giger,et al.  Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound , 2009, IEEE Transactions on Medical Imaging.

[24]  Chong-Sze Tong,et al.  Texture Classification Using Refined Histogram , 2010, IEEE Transactions on Image Processing.

[25]  Jie-Zhi Cheng,et al.  Computer-aided US diagnosis of breast lesions by using cell-based contour grouping. , 2010, Radiology.

[26]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[27]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[28]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[29]  Fang-Cheng Yeh,et al.  ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography. , 2010, Medical physics.

[30]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Marcos Salganicoff,et al.  Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models , 2011, Medical Image Anal..

[32]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[33]  J. Baker,et al.  Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. , 2011, Radiology.

[34]  B. van Ginneken,et al.  Computer-aided diagnosis: how to move from the laboratory to the clinic. , 2011, Radiology.

[35]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[36]  Wagner Coelho A. Pereira,et al.  Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound , 2012, IEEE Transactions on Medical Imaging.

[37]  Seong-Whan Lee,et al.  Latent feature representation with stacked auto-encoder for AD/MCI diagnosis , 2013, Brain Structure and Function.

[38]  Xiuhua Guo,et al.  Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data , 2013, PloS one.

[39]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  M. Giger,et al.  Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. , 2013, Annual review of biomedical engineering.

[41]  Jeon-Hor Chen,et al.  Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis , 2013, IEEE Transactions on Medical Imaging.

[42]  R. Fergus,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[43]  Shuiwang Ji,et al.  Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.

[44]  Ronald M. Summers,et al.  Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection , 2015, MICCAI.

[45]  Bram van Ginneken,et al.  Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box , 2015, Medical Image Anal..

[46]  Nima Tajbakhsh,et al.  Computer-Aided Pulmonary Embolism Detection Using a Novel Vessel-Aligned Multi-planar Image Representation and Convolutional Neural Networks , 2015, MICCAI.

[47]  Hao Chen,et al.  Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks , 2015, MICCAI.

[48]  Ronald M. Summers,et al.  Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.