Imbalanced Breast Cancer Classification Using Transfer Learning

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e. the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.

[1]  Ronald M. Summers,et al.  Anatomy-specific classification of medical images using deep convolutional nets , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[2]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[3]  Sameer Shrivastava,et al.  Multiplexed Autoantibody Signature for Serological Detection of Canine Mammary Tumours , 2018, Scientific Reports.

[4]  Fabio A. González,et al.  Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks , 2014, Medical Imaging.

[5]  Guodong Zhou,et al.  Active Learning for Imbalanced Sentiment Classification , 2012, EMNLP.

[6]  Guodong Zhou,et al.  Semi-Supervised Learning for Imbalanced Sentiment Classification , 2011, IJCAI.

[7]  Francisco Herrera,et al.  Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification , 2020, Inf. Sci..

[8]  Ying Ju,et al.  Finding the Best Classification Threshold in Imbalanced Classification , 2016, Big Data Res..

[9]  F. Calliada,et al.  Ultrasound guided fine-needle aspiration cytology of breast lesions. , 2011, Journal of ultrasound.

[10]  Jing Gao,et al.  On handling negative transfer and imbalanced distributions in multiple source transfer learning , 2014, SDM.

[11]  Linda G. Shapiro,et al.  Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks , 2018, Pattern Recognit..

[12]  Hanqi Zhuang,et al.  A Study on Automatic Detection of IDC Breast Cancer with Convolutional Neural Networks , 2018, 2018 International Conference on Computational Science and Computational Intelligence (CSCI).

[13]  Barbara Caputo,et al.  Multiclass transfer learning from unconstrained priors , 2011, 2011 International Conference on Computer Vision.

[14]  Xuan Liu,et al.  Classifying High Resolution Remote Sensing Images by Fine-Tuned VGG Deep Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Ronald M. Summers,et al.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[16]  Ee-Peng Lim,et al.  On strategies for imbalanced text classification using SVM: A comparative study , 2009, Decis. Support Syst..

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

[18]  Eugenio Culurciello,et al.  An Analysis of Deep Neural Network Models for Practical Applications , 2016, ArXiv.

[19]  Alexander A. Hernandez,et al.  Enhanced Deep Learning Approach for Predicting Invasive Ductal Carcinoma from Histopathology Images , 2019, 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD).

[20]  Krzysztof J. Cios,et al.  Computational intelligence in solving bioinformatics problems , 2005, Artif. Intell. Medicine.

[21]  Xiaogang Li,et al.  Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[22]  Francisco Herrera,et al.  SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering , 2015, Inf. Sci..

[23]  Rajesh Mehra,et al.  Breast cancer histology images classification: Training from scratch or transfer learning? , 2018, ICT Express.

[24]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[25]  Arun Kumar Sangaiah,et al.  Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer , 2020, Inf. Sci..

[26]  Melinda F Lerwill,et al.  Current Practical Applications of Diagnostic Immunohistochemistry in Breast Pathology , 2004, The American journal of surgical pathology.

[27]  Sameer Shrivastava,et al.  Surface plasmon resonance immunosensor for label-free detection of BIRC5 biomarker in spontaneously occurring canine mammary tumours , 2019, Scientific Reports.

[28]  Jinwen Ma,et al.  Imbalanced Histopathological Breast Cancer Image Classification with Convolutional Neural Network , 2018, 2018 14th IEEE International Conference on Signal Processing (ICSP).

[29]  V. Band,et al.  Histological, molecular and functional subtypes of breast cancers , 2010, Cancer biology & therapy.

[30]  Chandra Churh Chatterjee,et al.  A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks , 2019, 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT).

[31]  Gerald Schaefer,et al.  Cost-sensitive decision tree ensembles for effective imbalanced classification , 2014, Appl. Soft Comput..

[32]  Fernando De la Torre,et al.  Facing Imbalanced Data--Recommendations for the Use of Performance Metrics , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[35]  Hamid R. Tizhoosh,et al.  Projectron – A Shallow and Interpretable Network for Classifying Medical Images , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[36]  Jie Chen,et al.  Guidelines on the diagnosis and treatment of breast cancer (2011 edition). , 2012, Gland surgery.

[37]  Zhong-Qiu Zhao,et al.  A novel modular neural network for imbalanced classification problems , 2009, Pattern Recognit. Lett..

[38]  Junhao Wen,et al.  Fundus Image Classification Using VGG-19 Architecture with PCA and SVD , 2018, Symmetry.

[39]  Chandan K. Reddy,et al.  Transfer learning for class imbalance problems with inadequate data , 2015, Knowledge and Information Systems.

[40]  Kai Lu,et al.  Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study , 2019, ArXiv.

[41]  Jianmin Wang,et al.  Transfer Learning with Graph Co-Regularization , 2012, IEEE Transactions on Knowledge and Data Engineering.

[42]  Abdul Matin,et al.  Automatic System for Detecting Invasive Ductal Carcinoma Using Convolutional Neural Networks , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[43]  Arun Kumar Sangaiah,et al.  Multi-Fault Bearing Classification Using Sensors and ConvNet-Based Transfer Learning Approach , 2020, IEEE Sensors Journal.

[44]  Peter Devilee,et al.  Pathology and Genetics of Tumours of the Breast and Female Genital Organs , 2003 .

[45]  Sunil Mittal,et al.  Biosensors for breast cancer diagnosis: A review of bioreceptors, biotransducers and signal amplification strategies. , 2017, Biosensors & bioelectronics.

[46]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[47]  Francesca Bovolo,et al.  Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[49]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[50]  Constantino Carlos Reyes-Aldasoro,et al.  Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.

[51]  Linda G. Shapiro,et al.  Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions , 2019, JAMA network open.

[52]  Yuan-Hai Shao,et al.  An efficient weighted Lagrangian twin support vector machine for imbalanced data classification , 2014, Pattern Recognit..

[53]  Jonathon Shlens,et al.  A Learned Representation For Artistic Style , 2016, ICLR.

[54]  Andrew Janowczyk,et al.  Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases , 2016, Journal of pathology informatics.

[55]  Rakesh Kumar,et al.  Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning , 2019, Scientific Reports.

[56]  Francisco Herrera,et al.  Chain based sampling for monotonic imbalanced classification , 2019, Inf. Sci..

[57]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[58]  W. N. Street,et al.  Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates. , 1994, Cancer letters.

[59]  Joarder Kamruzzaman,et al.  z-SVM: An SVM for Improved Classification of Imbalanced Data , 2006, Australian Conference on Artificial Intelligence.

[60]  Phedias Diamandis,et al.  Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction , 2018, BMC Bioinformatics.

[61]  Jeremiah W. Johnson.  Dectecting Invasive Ductal Carcinoma with Semi-Supervised Conditional GANs , 2019, ArXiv.

[62]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

[63]  Sébastien Ourselin,et al.  Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning , 2017, IEEE Transactions on Medical Imaging.