Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Holger R. Roth , Le Lu , Jiamin Liu , Jianhua Yao , Ari Seff , Kevin Cherry , Lauren Kim , Ronald M. Summers

ABSTRACT

Inclinicalpracticeandmedicalimagingresearch, automated computer-aided detection (CADe) is an important tool. While many methods can achieve high sensitivities, they typically suffer from high false positives (FP) per patient. In this study, we describe a two-stage coarse-to-fine approach using CADe candidate generation systems that operate at high sensitivity rates (close to \(100\%\) recall). In a second stage, we reduce false positive numbers using state-of-the-art machine learning methods, namely deep convolutional neural networks (ConvNet). The ConvNets are trained to differentiate hard false positives from true-positives utilizing a set of 2D (two-dimensional) or 2.5D re-sampled views comprising random translations, rotations, and multi-scale observations around a candidate’s center coordinate. During the test phase, we apply the ConvNets on unseen patient data and aggregate all probability scores for lesions (or pathology). We found that this second stage is a highly selective classifier that is able to reject difficult false positives while retaining good sensitivity rates. The method was evaluated on three data sets (sclerotic metastases, lymph nodes, colonic polyps) with varying numbers patients (59, 176, and 1,186, respectively). Experiments show that the method is able to generalize to different applications and increasing data set sizes. Marked improvements are observed in all cases: sensitivities increased from 57 to 70%, from 43 to 77% and from 58 to 75% for sclerotic metastases, lymph nodes and colonic polyps, respectively, at low FP rates per patient (3 FPs/patient). More... »

PAGES

35-48

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  • Book

    TITLE

    Deep Learning and Convolutional Neural Networks for Medical Image Computing

    ISBN

    978-3-319-42998-4
    978-3-319-42999-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-42999-1_3

    DOI

    http://dx.doi.org/10.1007/978-3-319-42999-1_3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090664540


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