Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

Mordang, Jan-Jurre; Janssen, Tim; Bria, Alessandro; Kooi, Thijs; Gubern-Mérida, Albert; Karssemeijer, Nico

doi:10.1007/978-3-319-41546-8_5

Cited by 48 publications

(29 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In breast imaging, the majority of the existing publications are focusing on using CNNs for mammography. Dhungel et al [26] have implemented deep learning for segmentation of masses; Mordang et al [27] proposed the use of CNNs in microcalcification detection; and more recently, Ahn et al [28] proposed the use of CNNs in breast density estimation. In breast ultrasound imaging, Huynh et al [24] proposed the use of a transfer learning approach for ultrasound breast images classification.…”

Section: E Deep Learning For Breast Imagingmentioning

confidence: 99%

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Yap

Pons

Ganau

et al. 2018

IEEE J. Biomed. Health Inform.

737

404

View full text Add to dashboard Cite

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.

show abstract

Section: E Deep Learning For Breast Imagingmentioning

confidence: 99%

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Yap

Pons

Ganau

et al. 2018

IEEE J. Biomed. Health Inform.

737

404

View full text Add to dashboard Cite

show abstract

“…First, the sample size and examination type distribution for our observer evaluation study population were estimated on the basis of the results of a similar previous study (18), by using the unified method proposed by Hillis et al (20), to yield a study power greater than 0. examination (craniocaudal and mediolateral oblique views of both breasts). The system uses deep learning convolutional neural networks and features classifiers and image analysis algorithms to depict calcifications (21,22) and soft-tissue lesions (16,(23)(24)(25) in two different modules. Softtissue and calcification findings are later combined to determine suspicious region findings.…”

Section: Study Populationmentioning

confidence: 99%

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System

Rodríguez‐Ruiz

Krupiński

Mordang³

et al. 2019

Radiology

Self Cite

430

248

View full text Add to dashboard Cite

To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods: An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results: On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion: Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license.

show abstract

“…In breast imaging, the majority of the current publications are focusing on using CNNs for MG. Dhungel et al [37] have performed masses segmentation using deep learning; Mordang et al [38] introduced the use of CNNs in microcalcification detection; and lately, Ahn et al [39] suggested the use of CNNs in breast density evaluation. In breast US imaging, Huynh et al [15] suggested the use of a transfer learning approach for breast US images classification.…”

Section: Deep Learning For Breast Imagingmentioning

confidence: 99%

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

Al-Dhabyani¹,

Gomaa²,

Khaled³

et al. 2019

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

Cited by 48 publications

References 10 publications

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System

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

Contact Info

Product

Resources

About