Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms

By Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley. 2015

161 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. P. Heng
2. Qi-Yan Dou
3. Hao Chen
4. Lequan Yu
5. Xiaohui S. Xie
6. Jing Qin
7. Wentao Zhu
8. Andrew P. Bradley

In Computer Vision and Pattern Recognition

1. P. Heng
2. Xiaohui S. Xie
3. Qi-Yan Dou
4. Wentao Zhu
5. Lequan Yu
6. Hao Chen
7. Jing Qin
8. Gustavo Carneiro

Citations in the corpus

• Adversarial Deep Structural Networks for Mammographic Mass Segmentation (2016, methodology): “A recent work used the output from a CNN as an additional potential function, yielding the state-of-theart performance on mass segmentation [7].”
• Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, unknown): “INbreast 116 samples 2D Mammography [21, 55]”
• DCAN: Deep contour‐aware networks for object instance segmentation from histology images (2017, background): “They achieved state-of-the-art perormance in many detection and segmentation tasks in medial image computing ( Zheng et al., 2015; Dhungel et al., 2015; onneberger et al., 2015; Roth et al., 2015; Chen et al., 2015b; ou et al., 2016a; 2016b ).”
• Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks (2018, methodology): “[26] have implemented deep learning for segmentation of masses; Mordang et al.”
• Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. (2018, background): “…used to train a three layer CNN. 48 Similarly, since then, various studies have investigated different handcrafted features to train CNNs. 4,46,47,61,62 These methods conceptually work in a similar way: combining multiple modules and jointly training all parameters to demonstrate the power of…”
• 3D deeply supervised network for automated segmentation of volumetric medical images (2017, background): “While deep CNNs have chieved remarkable success in 2D medical image segmentations Xing et al., 2016; Chen et al., 2016d; Dhungel et al., 2015 ), it is still difficult task for CNNs to segment objects from 3D medical imges owing to the following mutually affected challenges.”
• A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification (2018, methodology): “In [45], CNN was utilized for breast cancer mass segmentation by comparing two different models: deep model based on SSVM and the other deep model based on conditional random field (CRF) classifier [45].”
• The Automated Learning of Deep Features for Breast Mass Classification from Mammograms (2016, methodology): “We also propose a fully automated CAD system for analysing breast masses from mammograms, comprising a detection [8] and a segmentation [9] steps, followed by the proposed deep learning models that classify breast masses.”
• DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation (2016, background): “Recently, deep neural networks are driving advances in image recognition related tasks in computer vision [21, 9, 7, 27, 29, 3] and medical image computing [10, 30, 31, 6, 12].”