A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons

Chen, Yanhong; Wang, Lijun; Luo, Ran; Wang, Shuang; Wang, Heng; Gao, Fei; Wang, Dengbin

doi:10.3389/fonc.2022.943415

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Already developed in the field of contrast-enhanced MRI [ 23 , 24 ], its application to CEM has only recently been explored in the literature. Existing studies in this area have used various state-of-the-art DL architectures for detection and segmentation tasks, but they have been limited by small datasets.…”

Section: Related Work On Cem-aimentioning

confidence: 99%

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

Jailin,

Mohamed,

Iordache

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Background: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. Materials & Methods: A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. Results: The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. Conclusion: The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability.

show abstract

Section: Related Work On Cem-aimentioning

confidence: 99%

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

Jailin,

Mohamed,

Iordache

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…However, the use of the deep learning models has often been limited to single modality of breast cancer imaging. Studies which have addressed abnormality classification on single modality have often considered magnetic resonance imaging (MRI) 2 , digital mammography, and ultrasound technology 3 , mammography 4 – 7 , contrast-enhanced mammography 8 , digital tomosynthesis 9 , sonography 10 , sonoelastography 11 , 12 , magnetic elastography, diffusion-weighted imaging 13 , magnetic spectroscopy, nuclear medicine 14 , 15 , image-guided breast biopsy 16 – 18 , optical imaging 19 , 20 , and microwave imaging 21 . The unimodal approach to detection of breast cancer disease is limited to using insufficient information in diagnosing physical condition.…”

Section: Introductionmentioning

confidence: 99%

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

Oyelade,

Irunokhai,

Wang

2024

Sci Rep

View full text Add to dashboard Cite

There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.

show abstract

“…However, deep-learning models are trained using large-scale, labeled data and neural network structures, where it is possible to learn features directly from the data, eliminating the need for feature extraction. Convolutional neural networks (CNNs or ConvNets) are used in deep neural networks (Figures 1 and 2) [44][45][46][47][48][49][50]. CNNs do not require manual feature extraction and have the advantage of eliminating the need to search for features during image classification.…”

Section: Introductionmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

View full text Add to dashboard Cite

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

show abstract

A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons

Cited by 7 publications

References 21 publications

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

Applications of Deep Learning for Drug Discovery Systems with BigData

Contact Info

Product

Resources

About