Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

2021 ◽  
Author(s):  
Wenwei Zhao ◽  
Meng Lou ◽  
Yunliang Qi ◽  
Yiming Wang ◽  
Chunbo Xu ◽  
...  
Keyword(s):  
Spatial Context ◽  
Breast Mass ◽  
Full Field ◽  
Mass Segmentation

scholarly journals Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

2021 ◽  
Author(s):  
Yutong Yan ◽  
Pierre-Henri Conze ◽  
Gwenolé Quellec ◽  
Mathieu Lamard ◽  
Beatrice Cochener ◽  
...  
Keyword(s):  
Breast Mass ◽  
Two Stage ◽  
Multi Scale ◽  
Mass Segmentation ◽  
User Intervention

scholarly journals Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network

2020 ◽  
Vol 61 ◽  
pp. 102027
Author(s):  
Michal Byra ◽  
Piotr Jarosik ◽  
Aleksandra Szubert ◽  
Michael Galperin ◽  
Haydee Ojeda-Fournier ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Mass ◽  
Mass Segmentation

Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm

2008 ◽  
pp. 948-957 ◽  
Author(s):  
Arianna Mencattini ◽  
Giulia Rabottino ◽  
Marcello Salmeri ◽  
Roberto Lojacono ◽  
Emanuele Colini
Keyword(s):  
Region Growing ◽  
Breast Mass ◽  
Mammographic Images ◽  
Mass Segmentation

Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets

2019 ◽  
pp. 608-620
Author(s):  
Qi Yin ◽  
Haiwei Pan ◽  
Bin Yang ◽  
Xiaofei Bian ◽  
Chunling Chen
Keyword(s):  
Breast Mass ◽  
Segmentation Algorithms ◽  
Mass Segmentation

Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT

2012 ◽  
pp. 697-704 ◽  
Author(s):  
Hsien-Chi Kuo ◽  
Maryellen L. Giger ◽  
Ingrid Reiser ◽  
John M. Boone ◽  
Karen K. Lindfors ◽  
...  
Keyword(s):  
Level Set ◽  
Breast Mass ◽  
Breast Ct ◽  
Contrast Enhanced ◽  
Mass Segmentation

scholarly journals AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256830
Author(s):  
Yeheng Sun ◽  
Yule Ji
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Medical Image Analysis ◽  
Training Data ◽  
Breast Mass ◽  
Weakly Supervised Learning ◽  
Learning Network ◽  
Teacher Student ◽  
Mass Segmentation ◽  
Weakly Supervised

Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model’s generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for extracting useful information from mammograms with weak annotations for efficient and accurate breast mass segmentation. Specifically, we adopt a weakly-supervised learning strategy in the Teacher to extract anatomy structure from mammograms with weak annotations by reconstructing the original image. Besides, knowledge distillation is used to suggest morphological differences between benign and malignant masses. Moreover, the prior knowledge learned from the Teacher is introduced to the Student in an end-to-end way, which improves the ability of the student network to locate and segment masses. Experiments on CBIS-DDSM have shown that our method yields promising performance compared with state-of-the-art alternative models for breast mass segmentation in terms of segmentation accuracy and IoU.

A novel method for breast mass segmentation: from superpixel to subpixel segmentation

2019 ◽  
Vol 30 (7-8) ◽  
pp. 1111-1122 ◽  
Author(s):  
Shenghua Gu ◽  
Yi Chen ◽  
Fangqing Sheng ◽  
Tianming Zhan ◽  
Yunjie Chen
Keyword(s):  
Breast Mass ◽  
Novel Method ◽  
Mass Segmentation

A Breast Mass Segmentation Method Based on FFC-MSPCNN

2021 ◽  
Author(s):  
Caixia Zhang ◽  
Jing Lian ◽  
Ruifeng Huang ◽  
Mingxuan Zhang
Keyword(s):  
Breast Mass ◽  
Segmentation Method ◽  
Mass Segmentation

A Novel Breast mass segmentation method based on patch merging and GHFCM

2015 ◽  
Vol 8 (10) ◽  
pp. 427-436
Author(s):  
Shenghua Gu ◽  
Yunjie Chen ◽  
Jin Wang ◽  
Jeong-Uk Kim
Keyword(s):  
Breast Mass ◽  
Segmentation Method ◽  
Mass Segmentation
Sign in / Sign up

Export Citation Format

Share Document