Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M. Iqbal; Ramli, Abdul Rahman

doi:10.17179/excli2016-701

Cited by 29 publications

(13 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MRI is only used for special cases (e.g., high-risk genetic mutation, multifocal disease), and its value for screening is currently under debate due to its high costs and the need for contrast agents [ 2 ]. One of the biggest challenges of US imaging is its high operator dependence [ 3 ]. This problem not only considers the repeatability of measurement, but also the user expertise.…”

Section: Introductionmentioning

confidence: 99%

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

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

View full text Add to dashboard Cite

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…Other imaging modalities, such as magnetic resonance imaging, are more applicable to high-risk mutation cases and, due to being costly, are not considered for screening. Ultrasound (US) imaging is another common screening modality, which is highly dependent on the experience and expertise of its operator [ 3 ]. There are inherent limitations concerning medical imaging such as mammography and ultrasound due to being a projection imaging modality and a small field of view, which causes difficulties in finding microcalcification deep inside the breast lesions.…”

Section: Introductionmentioning

confidence: 99%

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

Vigil

Barry

Amini

et al. 2022

Cancers

View full text Add to dashboard Cite

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.

show abstract

“…Differences in skill, knowledge, and understanding of breast US techniques among operators may lead to differences in diagnosis. Therefore, computer-aided diagnosis recently become an important research direction to assist radiologist with image interpretation [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3+

Shia

Hsu

Dai

et al. 2022

Sensors

View full text Add to dashboard Cite

In this study, an advanced semantic segmentation method and deep convolutional neural network was applied to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images, thereby facilitating image interpretation and diagnosis by providing radiologists an objective second opinion. A total of 684 images (380 benign and 308 malignant tumours) from 343 patients (190 benign and 153 malignant breast tumour patients) were analysed in this study. Six malignancy-related standardised BI-RADS features were selected after analysis. The DeepLab v3+ architecture and four decode networks were used, and their semantic segmentation performance was evaluated and compared. Subsequently, DeepLab v3+ with the ResNet-50 decoder showed the best performance in semantic segmentation, with a mean accuracy and mean intersection over union (IU) of 44.04% and 34.92%, respectively. The weighted IU was 84.36%. For the diagnostic performance, the area under the curve was 83.32%. This study aimed to automate identification of the malignant BI-RADS lexicon on breast ultrasound images to facilitate diagnosis and improve its quality. The evaluation showed that DeepLab v3+ with the ResNet-50 decoder was suitable for solving this problem, offering a better balance of performance and computational resource usage than a fully connected network and other decoders.

show abstract

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Cited by 29 publications

References 117 publications

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

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

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

Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3+

Contact Info

Product

Resources

About