Breast cancer remains the most diagnosed cancer in women. Advances in
medical imaging modalities ... more Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
Background: The accurate classification between malignant and benign breast lesions detected on m... more Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on the lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatics fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
Objective: Radiomics and deep transfer learning are two popular technologies used to develop comp... more 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 l...
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 2022
Radiomics and deep transfer learning have been attracting broad research interest in developing a... more Radiomics and deep transfer learning have been attracting broad research interest in developing and optimizing CAD
schemes of medical images. However, these two technologies are typically applied in different studies using different
image datasets. Advantages or potential limitations of applying these two technologies in CAD applications have not been
well investigated. This study aims to compare and assess these two technologies in classifying breast lesions. A
retrospective dataset including 2,778 digital mammograms is assembled in which 1,452 images depict malignant lesions
and 1,326 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. First, one scheme is
applied to segment lesions and compute radiomics features, while another scheme applies a pre-trained residual net
architecture (ResNet50) as a transfer learning model to extract automated features. Next, the same principal component
algorithm (PCA) is used to process both initially computed radiomics and automated features to create optimal feature
vectors by eliminating redundant features. Then, several support vector machine (SVM)-based classifiers are built using
the optimized radiomics or automated features. Each SVM model is trained and tested using a 10-fold cross-validation
method. Classification performance is evaluated using area under ROC curve (AUC). Two SVMs trained using radiomics
and automated features yield AUC of 0.77±0.02 and 0.85±0.02, respectively. In addition, SVM trained using the fused
radiomics and automated features does not yield significantly higher AUC. This study indicates that (1) using deep transfer
learning yields higher classification performance, and (2) radiomics and automated features contain highly correlated
information in lesion classification.
Advances in Intelligent Systems and Computing, 2014
The use of vehicular ad-hoc network (VANET) has been increasing immensely over the past few years... more The use of vehicular ad-hoc network (VANET) has been increasing immensely over the past few years. VANET has played a very important role in safety issues on roads. There are many routing protocols in VANET, out of which broadcasting protocol is used more frequently for sharing, traffic, weather and emergency, road conditions among vehicles and delivering advertisements and announcements. Several types of broadcasting protocols have been proposed, out of which DV-CAST protocol and edge aware epidemic protocol (EAEP) are two such types that have come into force recently. It is said that these two protocols can be of great use later but their performances are yet to be explored in different scenarios. So in this paper the performance of DV-CAST protocol and EAEP are discussed, in well-connected and totally disconnected highway and city scenarios respectively, and a conclusion was reached about which protocol works better in different scenarios. The evaluation includes the measurements of the parameters such as packet drop and throughput. NCTUns 6.0 was used as the network simulator. NCTUns is a high fidelity and extensible network simulator and emulator which helps to simulate complex scenarios. The challenges and future perspectives of broadcasting protocols are also discussed in this paper. This work helps the researchers, who are currently working on other routing protocols in VANET, to come to a decision about the best routing protocols that are to be used in different scenarios.
Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically inclu... more Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically inclu... more Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
Breast cancer remains the most diagnosed cancer in women. Advances in
medical imaging modalities ... more Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
Background: The accurate classification between malignant and benign breast lesions detected on m... more Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on the lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatics fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
Objective: Radiomics and deep transfer learning are two popular technologies used to develop comp... more 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 l...
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 2022
Radiomics and deep transfer learning have been attracting broad research interest in developing a... more Radiomics and deep transfer learning have been attracting broad research interest in developing and optimizing CAD
schemes of medical images. However, these two technologies are typically applied in different studies using different
image datasets. Advantages or potential limitations of applying these two technologies in CAD applications have not been
well investigated. This study aims to compare and assess these two technologies in classifying breast lesions. A
retrospective dataset including 2,778 digital mammograms is assembled in which 1,452 images depict malignant lesions
and 1,326 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. First, one scheme is
applied to segment lesions and compute radiomics features, while another scheme applies a pre-trained residual net
architecture (ResNet50) as a transfer learning model to extract automated features. Next, the same principal component
algorithm (PCA) is used to process both initially computed radiomics and automated features to create optimal feature
vectors by eliminating redundant features. Then, several support vector machine (SVM)-based classifiers are built using
the optimized radiomics or automated features. Each SVM model is trained and tested using a 10-fold cross-validation
method. Classification performance is evaluated using area under ROC curve (AUC). Two SVMs trained using radiomics
and automated features yield AUC of 0.77±0.02 and 0.85±0.02, respectively. In addition, SVM trained using the fused
radiomics and automated features does not yield significantly higher AUC. This study indicates that (1) using deep transfer
learning yields higher classification performance, and (2) radiomics and automated features contain highly correlated
information in lesion classification.
Advances in Intelligent Systems and Computing, 2014
The use of vehicular ad-hoc network (VANET) has been increasing immensely over the past few years... more The use of vehicular ad-hoc network (VANET) has been increasing immensely over the past few years. VANET has played a very important role in safety issues on roads. There are many routing protocols in VANET, out of which broadcasting protocol is used more frequently for sharing, traffic, weather and emergency, road conditions among vehicles and delivering advertisements and announcements. Several types of broadcasting protocols have been proposed, out of which DV-CAST protocol and edge aware epidemic protocol (EAEP) are two such types that have come into force recently. It is said that these two protocols can be of great use later but their performances are yet to be explored in different scenarios. So in this paper the performance of DV-CAST protocol and EAEP are discussed, in well-connected and totally disconnected highway and city scenarios respectively, and a conclusion was reached about which protocol works better in different scenarios. The evaluation includes the measurements of the parameters such as packet drop and throughput. NCTUns 6.0 was used as the network simulator. NCTUns is a high fidelity and extensible network simulator and emulator which helps to simulate complex scenarios. The challenges and future perspectives of broadcasting protocols are also discussed in this paper. This work helps the researchers, who are currently working on other routing protocols in VANET, to come to a decision about the best routing protocols that are to be used in different scenarios.
Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically inclu... more Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically inclu... more Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
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Papers by warid Islam
medical imaging modalities and technologies have greatly aided in the early
detection of breast cancer and the decline of patient mortality rates. However,
reading and interpreting breast images remains difficult due to the high
heterogeneity of breast tumors and fibro-glandular tissue, which results in
lower cancer detection sensitivity and specificity and large inter-reader
variability. In order to help overcome these clinical challenges, researchers
have made great efforts to develop computer-aided detection and/or
diagnosis (CAD) schemes of breast images to provide radiologists with
decision-making support tools. Recent rapid advances in high throughput
data analysis methods and artificial intelligence (AI) technologies, particularly
radiomics and deep learning techniques, have led to an exponential increase in
the development of new AI-based models of breast images that cover a broad
range of application topics. In this review paper, we focus on reviewing recent
advances in better understanding the association between radiomics features
and tumor microenvironment and the progress in developing new AI-based
quantitative image feature analysis models in three realms of breast cancer:
predicting breast cancer risk, the likelihood of tumor malignancy, and tumor
response to treatment. The outlook and three major challenges of applying
new AI-based models of breast images to clinical practice are also discussed.
Through this review we conclude that although developing new AI-based
models of breast images has achieved significant progress and promising
results, several obstacles to applying these new AI-based models to clinical
practice remain. Therefore, more research effort is needed in future studies.
and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on the lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the
area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep
transfer learning technology attracted broad research interest in medical-imaging informatics fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
schemes of medical images. However, these two technologies are typically applied in different studies using different
image datasets. Advantages or potential limitations of applying these two technologies in CAD applications have not been
well investigated. This study aims to compare and assess these two technologies in classifying breast lesions. A
retrospective dataset including 2,778 digital mammograms is assembled in which 1,452 images depict malignant lesions
and 1,326 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. First, one scheme is
applied to segment lesions and compute radiomics features, while another scheme applies a pre-trained residual net
architecture (ResNet50) as a transfer learning model to extract automated features. Next, the same principal component
algorithm (PCA) is used to process both initially computed radiomics and automated features to create optimal feature
vectors by eliminating redundant features. Then, several support vector machine (SVM)-based classifiers are built using
the optimized radiomics or automated features. Each SVM model is trained and tested using a 10-fold cross-validation
method. Classification performance is evaluated using area under ROC curve (AUC). Two SVMs trained using radiomics
and automated features yield AUC of 0.77±0.02 and 0.85±0.02, respectively. In addition, SVM trained using the fused
radiomics and automated features does not yield significantly higher AUC. This study indicates that (1) using deep transfer
learning yields higher classification performance, and (2) radiomics and automated features contain highly correlated
information in lesion classification.
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
medical imaging modalities and technologies have greatly aided in the early
detection of breast cancer and the decline of patient mortality rates. However,
reading and interpreting breast images remains difficult due to the high
heterogeneity of breast tumors and fibro-glandular tissue, which results in
lower cancer detection sensitivity and specificity and large inter-reader
variability. In order to help overcome these clinical challenges, researchers
have made great efforts to develop computer-aided detection and/or
diagnosis (CAD) schemes of breast images to provide radiologists with
decision-making support tools. Recent rapid advances in high throughput
data analysis methods and artificial intelligence (AI) technologies, particularly
radiomics and deep learning techniques, have led to an exponential increase in
the development of new AI-based models of breast images that cover a broad
range of application topics. In this review paper, we focus on reviewing recent
advances in better understanding the association between radiomics features
and tumor microenvironment and the progress in developing new AI-based
quantitative image feature analysis models in three realms of breast cancer:
predicting breast cancer risk, the likelihood of tumor malignancy, and tumor
response to treatment. The outlook and three major challenges of applying
new AI-based models of breast images to clinical practice are also discussed.
Through this review we conclude that although developing new AI-based
models of breast images has achieved significant progress and promising
results, several obstacles to applying these new AI-based models to clinical
practice remain. Therefore, more research effort is needed in future studies.
and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on the lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the
area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep
transfer learning technology attracted broad research interest in medical-imaging informatics fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.
schemes of medical images. However, these two technologies are typically applied in different studies using different
image datasets. Advantages or potential limitations of applying these two technologies in CAD applications have not been
well investigated. This study aims to compare and assess these two technologies in classifying breast lesions. A
retrospective dataset including 2,778 digital mammograms is assembled in which 1,452 images depict malignant lesions
and 1,326 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. First, one scheme is
applied to segment lesions and compute radiomics features, while another scheme applies a pre-trained residual net
architecture (ResNet50) as a transfer learning model to extract automated features. Next, the same principal component
algorithm (PCA) is used to process both initially computed radiomics and automated features to create optimal feature
vectors by eliminating redundant features. Then, several support vector machine (SVM)-based classifiers are built using
the optimized radiomics or automated features. Each SVM model is trained and tested using a 10-fold cross-validation
method. Classification performance is evaluated using area under ROC curve (AUC). Two SVMs trained using radiomics
and automated features yield AUC of 0.77±0.02 and 0.85±0.02, respectively. In addition, SVM trained using the fused
radiomics and automated features does not yield significantly higher AUC. This study indicates that (1) using deep transfer
learning yields higher classification performance, and (2) radiomics and automated features contain highly correlated
information in lesion classification.
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.
learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest
(ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach
to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed
ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image.
Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region
growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on
GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI
and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from
original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with
a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as
an evaluation index, our study results reveal no significant difference between AUC values computed using classification
scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing
the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed
using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these
features can significantly improve CAD performance.