Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer

Chen, Xuxin; Zargari, Abolfazl; Hollingsworth, Alan B.; Liu, Hong; Qiu, Yuchen

doi:10.1016/j.cmpb.2019.104995

Cited by 26 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheme initially computes 59 global mammographic image features, followed by applying a particle swarm optimization algorithm to search for optimal features and training a support vector machine model to predict the likelihood of malignancy. When using a relatively small dataset involving 134 malignant and 141 benign cases, the model yields a performance of AUC = 0.79 ± 0.07 [58], which is highly comparable to the performance of applying tumor-based CAD schemes in classifying malignant and benign tumors [26].…”

Section: Discussionmentioning

confidence: 83%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

Self Cite

View full text Add to dashboard Cite

In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

show abstract

Section: Discussionmentioning

confidence: 83%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays, neural networks and deep learning models are important parts of detection, prediction, classification, segmentation, and recognition systems with different applications [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. U-Net is a convolutional neural network (CNN) architecture used for accurate and fast image segmentation [10].…”

Section: B U-net Structurementioning

confidence: 99%

Deep learning denoising for EOG artifacts removal from EEG signals

Mashhadi

Khuzani

Heidari

et al. 2020

2020 IEEE Global Humanitarian Technology Conference (GHTC)

View full text Add to dashboard Cite

There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs).

show abstract

“…For CAD of mammograms, the computer-aided detection (CADe) schemes of suspicious lesion detection have been implemented in many medical centers or hospitals to assist radiologists in reading screening mammograms [ 7 ]. However, although great research effort has been made to develop computer-aided diagnosis (CADx) schemes of lesion classification [ 8 , 9 ], no CADx schemes have been approved and accepted in clinical practice. In this study, we focus on developing computer-aided diagnosis schemes of mammograms in order to help improve accuracy of lesion classification.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.

show abstract

Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer

Cited by 26 publications

References 33 publications

Developing global image feature analysis models to predict cancer risk and prognosis

Developing global image feature analysis models to predict cancer risk and prognosis

Deep learning denoising for EOG artifacts removal from EEG signals

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

Contact Info

Product

Resources

About