Search

Your search keyword '"Benedetta Tafuri"' showing total 2 results
Start Over Author "Benedetta Tafuri" Journal applied sciences
2 results on '"Benedetta Tafuri"'

1. Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach

Author

Luana Conte, Benedetta Tafuri, Maurizio Portaluri, Alessandro Galiano, Eleonora Maggiulli, and Giorgio De Nunzio

Subjects
breast cancer, Radiomics, machine learning, deep learning, segmentation, in situ breast cancer, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups—in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels—(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE–MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.

Published
2020
Full Text
View/download PDF

2. Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach

Author

Maurizio Portaluri, Eleonora Maggiulli, Benedetta Tafuri, Alessandro Galiano, Giorgio De Nunzio, Luana Conte, Conte, L., Tafuri, B., Portaluri, M., Galiano, A., Maggiulli, E., and De Nunzio, G.

Subjects
In situ, infiltrative breast cancer, 02 engineering and technology, lcsh:Technology, 030218 nuclear medicine & medical imaging, lcsh:Chemistry, 0302 clinical medicine, Breast cancer, Segmentation, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, oncology_oncogenics, Fluid Flow and Transfer Processes, medicine.diagnostic_test, General Engineering, food and beverages, lcsh:QC1-999, Computer Science Applications, machine learning, 020201 artificial intelligence & image processing, Radiology, Radiomic, In situ breast cancer, medicine.medical_specialty, Feature extraction, Infiltrative breast cancer, 03 medical and health sciences, breast cancer, Machine learning, Carcinoma, medicine, Radiomics, business.industry, lcsh:T, Process Chemistry and Technology, Deep learning, fungi, segmentation, Cancer, deep learning, Magnetic resonance imaging, medicine.disease, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Artificial intelligence, business, lcsh:Engineering (General). Civil engineering (General), in situ breast cancer, lcsh:Physics
Abstract

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups&mdash, in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels&mdash, (1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection, and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE&ndash, MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results