Literature DB >> 19144483

Processed images in human perception: a case study in ultrasound breast imaging.

Moi Hoon Yap1, Eran Edirisinghe, Helmut Bez.   

Abstract

Two main research efforts in early detection of breast cancer include the development of software tools to assist radiologists in identifying abnormalities and the development of training tools to enhance their skills. Medical image analysis systems, widely known as Computer-Aided Diagnosis (CADx) systems, play an important role in this respect. Often it is important to determine whether there is a benefit in including computer-processed images in the development of such software tools. In this paper, we investigate the effects of computer-processed images in improving human performance in ultrasound breast cancer detection (a perceptual task) and classification (a cognitive task). A survey was conducted on a group of expert radiologists and a group of non-radiologists. In our experiments, random test images from a large database of ultrasound images were presented to subjects. In order to gather appropriate formal feedback, questionnaires were prepared to comment on random selections of original images only, and on image pairs consisting of original images displayed alongside computer-processed images. We critically compare and contrast the performance of the two groups according to perceptual and cognitive tasks. From a Receiver Operating Curve (ROC) analysis, we conclude that the provision of computer-processed images alongside the original ultrasound images, significantly improve the perceptual tasks of non-radiologists but only marginal improvements are shown in the perceptual and cognitive tasks of the group of expert radiologists. Copyright 2008 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19144483     DOI: 10.1016/j.ejrad.2008.11.007

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network.

Authors:  Ruoyun Liu; Shichong Zhou; Yi Guo; Yuanyuan Wang; Cai Chang
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

2.  Breast ultrasound lesions recognition: end-to-end deep learning approaches.

Authors:  Moi Hoon Yap; Manu Goyal; Fatima M Osman; Robert Martí; Erika Denton; Arne Juette; Reyer Zwiggelaar
Journal:  J Med Imaging (Bellingham)       Date:  2018-10-10

3.  Computer-aided diagnostic models in breast cancer screening.

Authors:  Turgay Ayer; Mehmet Us Ayvaci; Ze Xiu Liu; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Imaging Med       Date:  2010-06-01

4.  Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.

Authors:  Nicolle Vigil; Madeline Barry; Arya Amini; Moulay Akhloufi; Xavier P V Maldague; Lan Ma; Lei Ren; Bardia Yousefi
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

5.  Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.

Authors:  Zuzanna Anna Magnuska; Benjamin Theek; Milita Darguzyte; Moritz Palmowski; Elmar Stickeler; Volkmar Schulz; Fabian Kießling
Journal:  Cancers (Basel)       Date:  2022-01-06       Impact factor: 6.639

  5 in total

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