CR‐SSL : A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation

2021 ◽  
Author(s):  
Arnab Kumar Mishra ◽  
Pinki Roy ◽  
Sivaji Bandyopadhyay ◽  
Sujit Kumar Das
Keyword(s):  
Supervised Learning ◽  
Breast Ultrasound ◽  
Tumor Segmentation

scholarly journals Tumor Segmentation in Breast Ultrasound Image by Means of Res Path Combined with Dense Connection Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1565
Author(s):  
Kailuo Yu ◽  
Sheng Chen ◽  
Yanghuai Chen
Keyword(s):  
False Positive Rate ◽  
Ultrasound Image ◽  
True Positive Rate ◽  
Breast Ultrasound ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Feature Maps ◽  
Test Models ◽  
Positive Rate ◽  
The Difference

Over the past few years, researchers have demonstrated the possibilities to use the Computer-Aided Diagnosis (CAD) to provide a preliminary diagnosis. Recently, it is also becoming increasingly common for doctors and computer practitioners to collaborate on developing CAD. Since the early diagnosis of breast cancer is the most critical step, a precise segmentation of breast tumor with accurate edge and shape is vital for accurate diagnoses and reduction in the patients’ pain. In view of the deficient accuracy of existing method, we proposed a novel method based on U-Net to improve the tumor segmentation accuracy in breast ultrasound images. First, Res Path was introduced into the U-Net to reduce the difference between the feature maps of the encoder and decoder. Then, a new connection, dense block from the input of the feature maps in the encoding-to-decoding section, was added to reduce the feature information loss and alleviate the vanishing gradient problem. A breast ultrasound database, which contains 538 tumor images, from Xinhua Hospital in Shanghai and marked by two professional doctors was used to train and test models. We, using ten-fold cross-validation method, compared the U-Net, U-Net with Res Path, and the proposed method to verify the improvements. The results demonstrated an overall improvement by the proposed approach when compared with the other in terms of true-positive rate, false-positive rate, Hausdorff distance indices, Jaccard similarity, and Dice coefficients.

Multiscale superpixel classification for tumor segmentation in breast ultrasound images

2012 ◽  
Author(s):  
Zhihui Hao ◽  
Qiang Wang ◽  
Haibing Ren ◽  
Kuanhong Xu ◽  
Yeong Kyeong Seong ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Tumor Segmentation ◽  
Ultrasound Images

Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism

Ultrasonics ◽  
2021 ◽  
Vol 110 ◽  
pp. 106271
Author(s):  
Pan Pan ◽  
Houjin Chen ◽  
Yanfeng Li ◽  
Naxin Cai ◽  
Lin Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Breast Ultrasound ◽  
Attention Mechanism ◽  
Tumor Segmentation ◽  
Bidirectional Lstm

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN

2020 ◽  
Author(s):  
Yang Lei ◽  
Xiuxiu He ◽  
Jincao Yao ◽  
Tonghe Wang ◽  
Lijing Wang ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Breast Ultrasound ◽  
Tumor Segmentation
Sign in / Sign up

Export Citation Format

Share Document