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Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the ha...

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Published in:Journal of Digital Imaging Vol. 30; no. 4; pp. 499 - 506
Main Authors: Teare, Philip, Fishman, Michael, Benzaquen, Oshra, Toledano, Eyal, Elnekave, Eldad
Format: diagnostic images pictorial tables/charts Journal Article
Published: Springer Nature Aug2017
Online Access:View this record in EBSCOhost
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      dt: Aug2017
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-017-9993-2
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        atl: Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.
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          Teare, Philip
          Fishman, Michael
          Benzaquen, Oshra
          Toledano, Eyal
          Elnekave, Eldad
        affil: Zebra Medical Vision LTD , Shfayim Israel
      sug:
        subj:
          Mammography Methods
          Image Interpretation, Computer Assisted
          Neural Networks (Computer)
          Human
          Algorithms
          Breast Neoplasms Diagnosis
          ROC Curve
      ab: Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers. Here we present two novel techniques to address inherent challenges in the application of ML to the domain of mammography. We describe the use of genetic search of image enhancement methods, leading us to the use of a novel form of false color enhancement through contrast limited adaptive histogram equalization (CLAHE), as a method to optimize mammographic feature representation. We also utilize dual deep convolutional neural networks at different scales, for classification of full mammogram images and derivative patches combined with a random forest gating network as a novel architectural solution capable of discerning malignancy with a specificity of 0.91 and a specificity of 0.80. To our knowledge, this represents the first automatic stand-alone mammography malignancy detection algorithm with sensitivity and specificity performance similar to that of expert radiologists.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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