Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

By Ravi K. Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Mark A. Helvie, Jun Wei, Kenny H. Cha. 2016

270 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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. Ashirbani Saha
2. Kenji Suzuki
3. Francesco Ciompi
4. Mateusz Buda
5. Xiaosong Wang
6. Maciej A. Mazurowski
7. Lubomir M. Hadjiiski
8. J. A. van der Laak

In Pulmonary and Respiratory Medicine

1. Kenji Suzuki
2. B. van Ginneken
3. Ronald M. Summers
4. Xiaosong Wang
5. Maciej A. Mazurowski
6. Mustafa R. Bashir
7. Mateusz Buda
8. Ashirbani Saha

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. Ronald M. Summers
3. Lubomir M. Hadjiiski
4. Xiaosong Wang
5. Kenji Suzuki
6. Kenny H. Cha
7. Francesco Ciompi
8. Maciej A. Mazurowski

Citations in the corpus

• A survey on deep learning in medical image analysis (2017, methodology): “To combat this, groups have tried to combine fCNNs with graphical models like MRFs (Shakeri et al., 2016; Song et al., 2015) and Conditional Random Fields (CRFs) (Alansary et al., 2016; Cai et al., 2016a; Christ et al., 2016; Dou et al., 2016c; Fu et al., 2016a; Gao et al., 2016c) to refine the segme”
• Overview of deep learning in medical imaging (2017, methodology): “After the introduction of the term ‘‘deep learning’’, a CNN was used for classification of masses and non-masses in digital breast tomosynthesis images [96].”
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “In other applications, models have been pretrained using other medical imaging datasets to detect masses in digital breast tomosynthesis images.(80) The same network architectures can be used for the second phase, as in a regular classification task (eg, VGG,(34) GoogLeNet,(81) Inception,(38) ResNet”
• A review on deep learning in medical image analysis (2021, background): “The fact that stochastic gradient descent minimizes generalization error is easiest to see in the online training situation, where examples or mini lots are taken from a data stream [93].”
• Current Applications and Future Impact of Machine Learning in Radiology. (2018, unknown): “Several studies have shown the diagnostic value of machine learning techniques in different breast imaging modalities including mammography (36), US (37), MRI (38), and tomosynthesis (39).”
• Deep learning in medical imaging and radiation therapy. (2018, methodology): “Samala et al. first pretrained a DCNN on Ima-geNet 198 or a larger mammogram dataset 17 and then fine-tuned on a digital breast tomosynthesis (DBT) dataset for the classification and detection of masses on DBT. Zheng et al. 254 pretrained on synthetic data and retrained on clinical data for 2D – 3D…”