Literature DB >> 20813633

On combining computer-aided detection systems.

Meindert Niemeijer1, Marco Loog, Michael David Abramoff, Max A Viergever, Mathias Prokop, Bram van Ginneken.   

Abstract

Computer-aided detection (CAD) is increasingly used in clinical practice and for many applications a multitude of CAD systems have been developed. In practice, CAD systems have different strengths and weaknesses and it is therefore interesting to consider their combination. In this paper, we present generic methods to combine multiple CAD systems and investigate what kind of performance increase can be expected. Experimental results are presented using data from the ANODE09 and ROC09 online CAD challenges for the detection of pulmonary nodules in computed tomography scans and red lesions in retinal images, respectively. For both applications, combination results in a large and significant increase in performance when compared to the best individual CAD system.

Mesh:

Year:  2010        PMID: 20813633     DOI: 10.1109/TMI.2010.2072789

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  8 in total

1.  Combination of computer-aided detection algorithms for automatic lung nodule identification.

Authors:  Niccolò Camarlinghi; Ilaria Gori; Alessandra Retico; Roberto Bellotti; Paolo Bosco; Piergiorgio Cerello; Gianfranco Gargano; Ernesto Lopez Torres; Rosario Megna; Marco Peccarisi; Maria Evelina Fantacci
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-07-08       Impact factor: 2.924

2.  3D deep learning for detecting pulmonary nodules in CT scans.

Authors:  Ross Gruetzemacher; Ashish Gupta; David Paradice
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

3.  Fully automated detection of breast cancer in screening MRI using convolutional neural networks.

Authors:  Mehmet Ufuk Dalmış; Suzan Vreemann; Thijs Kooi; Ritse M Mann; Nico Karssemeijer; Albert Gubern-Mérida
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-11

4.  Validation of tablet-based evaluation of color fundus images.

Authors:  Mark Christopher; Daniela C Moga; Stephen R Russell; James C Folk; Todd Scheetz; Michael D Abràmoff
Journal:  Retina       Date:  2012-09       Impact factor: 4.256

5.  How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study.

Authors:  Jeong Woo Son; Ji Young Hong; Yoon Kim; Woo Jin Kim; Dae-Yong Shin; Hyun-Soo Choi; So Hyeon Bak; Kyoung Min Moon
Journal:  Cancers (Basel)       Date:  2022-06-28       Impact factor: 6.575

6.  Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network.

Authors:  Yi-Ming Xu; Teng Zhang; Hai Xu; Liang Qi; Wei Zhang; Yu-Dong Zhang; Da-Shan Gao; Mei Yuan; Tong-Fu Yu
Journal:  Cancer Manag Res       Date:  2020-04-29       Impact factor: 3.989

7.  Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.

Authors:  Sunyi Zheng; Ludo J Cornelissen; Xiaonan Cui; Xueping Jing; Raymond N J Veldhuis; Matthijs Oudkerk; Peter M A van Ooijen
Journal:  Med Phys       Date:  2020-12-30       Impact factor: 4.071

8.  Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets.

Authors:  Jinglun Liang; Guoliang Ye; Jianwen Guo; Qifan Huang; Shaohui Zhang
Journal:  Front Public Health       Date:  2021-05-19
  8 in total

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