Processed images in human perception: A case study in ultrasound breast imaging

Yap, Moi Hoon; Edirisinghe, Eran A.; Bez, Helmut E.

doi:10.1016/j.ejrad.2008.11.007

Cited by 31 publications

(22 citation statements)

References 7 publications

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“…Thus, Computer-Aided Diagnosis (CAD) could be beneficial to help radiologists in the US-based detection of breast cancer, minimizing the effect of the operator-dependent nature of US imaging. Different studies have investigated the influence of CAD on diagnostics [4], [5] and showed that CAD is an important tool to improve the diagnostic sensitivity and specificity. The first challenge in any CAD is the ability to locate the lesion.…”

Section: Introductionmentioning

confidence: 99%

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Yap

Pons

Ganau

et al. 2018

IEEE J. Biomed. Health Inform.

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404

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Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

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Section: Introductionmentioning

confidence: 99%

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Yap

Pons

Ganau

et al. 2018

IEEE J. Biomed. Health Inform.

Self Cite

704

404

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“…Therefore, Computer Aided Diagnosis (CAD) can help radiologists in the US-based classification and detection of breast cancer, reducing the influence of the hand-dependence of US imaging. Some researches have studied the effect of CAD diagnostics [5], [6] and noted that CAD is a robust tool to enhance diagnostic specificity and sensitivity. Breast US research has a shortage of benchmark dataset which results in limiting the advancement of recent algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Al-Dhabyani¹,

Gomaa²,

Khaled³

et al. 2019

IJACSA

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Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagnosis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women's Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.

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“…In a recent study, Yap et al . designed a survey to evaluate the benefits of computerized processing of US images in improving the readers’ performance of breast cancer detection and classification [62]. The study demonstrated marginal improvements in classification when computer-processed US images alongside the originals are used in distinguishing benign from malignant lesions.…”

Section: Us Cadx Modelsmentioning

confidence: 99%

Computer-aided diagnostic models in breast cancer screening

Ayer

Ayvaci

Liu

et al. 2010

Imaging in Medicine

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Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.Keywords breast cancer; computer-aided detection; computer-aided diagnosis; mammography; MRI; ultrasound Radiological imaging, which often includes mammography, ultrasound (US) and MRI, is the most effective means, to date, for early detection of breast cancer [1]. However, differentiating between benign and malignant findings is difficult.Successful breast cancer diagnosis requires systematic image analysis, characterization and integration of numerous clinical and mammographic variables [2], which is a difficult and error-prone task for physicians. This leads to low positive predictive value of imaging interpretation [3].The integration of computer models into the radiological imaging interpretation process can increase the accuracy of image interpretation. There are two broad categories of computer models in breast cancer diagnosis: computer-aided detection (CADe) and computer-aided The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. NIH Public Access Author ManuscriptImaging Med. Author manuscript; available in PMC 2011 April 1. Published in final edited form as:Imaging Med. Mammography CADx modelsEarly work involving CADx models in mammography interpretation dates back to 1993. A summary list for primary mammography CADx models is presented in Huo et al. also used ANNs to classify mass lesions detected on screen-film mammograms [32,33]. They automated the feature extraction process to reduce the intra-observer variability [28,34]. In a follow-up study, Huo et al. used different sets of data for training and testing instead of a single database [35]. Their database included 50 biopsy-proven malignant masses, 50 biopsy-proven benign masses and ten cysts proved by fine needle aspirat...

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Processed images in human perception: A case study in ultrasound breast imaging

Cited by 31 publications

References 7 publications

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Computer-aided diagnostic models in breast cancer screening

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