2017
DOI: 10.1088/1361-6560/aa82ec
|View full text |Cite
|
Sign up to set email alerts
|

A deep learning framework for supporting the classification of breast lesions in ultrasound images

Abstract: In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augm… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
200
3
2

Year Published

2018
2018
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 303 publications
(205 citation statements)
references
References 60 publications
0
200
3
2
Order By: Relevance
“…All these results are comparable with the results reported in previous papers, where the AUC values of around 0.85 were obtained. In one of the studies, the GoogleLeNet was fine‐tuned to classify breast masses in US images . The authors achieved high AUC value of 0.96.…”
Section: Discussionmentioning
confidence: 99%
See 4 more Smart Citations
“…All these results are comparable with the results reported in previous papers, where the AUC values of around 0.85 were obtained. In one of the studies, the GoogleLeNet was fine‐tuned to classify breast masses in US images . The authors achieved high AUC value of 0.96.…”
Section: Discussionmentioning
confidence: 99%
“…Due to limited medical datasets, it is usually more efficient to use transfer learning and adjust a pretrained deep model to address the classification problem of interest. Transfer learning methods were employed for breast mass classification and segmentation in several studies . Additionally, deep learning was used to detect breast lesions and differentiate breast masses with shear‐wave elastography .…”
Section: Introductionmentioning
confidence: 99%
See 3 more Smart Citations