2019/08/22 · This section reports the results of the approaches described in Sect. 3 with and without applying the MEDx3 Motion Correction Technique.

To this aim, we evaluated the effectiveness of a MCT both for the classification and for the segmentation of breast lesions in DCE-MRI by means of some ...

To this aim, we evaluated the effectiveness of a MCT both for the classification and for the segmentation of breast lesions in DCE-MRI by means of some ...

2019. Evaluating impacts of motion correction on deep learning approaches for breast DCE-MRI segmentation and classification. A Galli, M Gravina, S Marrone, G ...

In this research, we propose an automatic methodology capable of detecting tumors and classifying their malignancy in a DCE-MRI breast image.

2020. Evaluating impacts of motion correction on deep learning approaches for breast DCE-MRI segmentation and classification. A Galli, M Gravina, S Marrone, G ...

2020/08/28 · The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI.

Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification. Chapter. Aug 2019.

2023/12/18 · Automated breast tumor segmentation in DCE-MRI using deep learning. ... Weakly supervised deep learning approach to breast MRI assessment.

The aim of this work is to apply a suitably modified convolutional neural network for fully-automating the non-trivial breast tissues segmentation task in ...