Pairwise domain adaptation module for CNN-based 2-D/3-D registration

By Jiannan Zheng, Shun Miao, Z. Jane Wang, Rui Liao. 2018

38 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, 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 Biomedical Engineering

1. Arvid Lundervold
2. Ronald M. Summers
3. Meghavi Rana
4. Megha Bhushan

In Computer Vision and Pattern Recognition

1. Xiaosong Wang
2. Arvid Lundervold
3. Maryellen Lissak Giger
4. Walter J. Curran
5. Yabo Fu
6. Tian Liu
7. Berkman Sahiner
8. Ronald M. Summers

In Radiology, Nuclear Medicine and Imaging

1. Lubomir M. Hadjiiski
2. Xiaosong Wang
3. Ronald M. Summers
4. Arvid Lundervold
5. Kenny H. Cha
6. Berkman Sahiner
7. Karen Drukker
8. Aria Pezeshk

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “registration frame-work for registration of preoperative 3D data and intraoperative 2D X-ray images in image-guided therapy [99]; real-time prostate segmentation during targeted prostate biopsy, utilizing temporal information in the series of ultrasound images [100].”
• Deep learning in medical imaging and radiation therapy. (2018, methodology): “254 To narrow the gap, the authors proposed a domain adaptation method by learning domain invariant features with only a few paired real and synthetic data.”
• A review of multimodal image matching: Methods and applications (2021, unknown): “odules [99] or statistical appearance models [100] are often used”
• Deep learning in medical image registration: a review (2019, background): “In addition, they used a dilated FCN-based training mechanism to reduce the degree of freedom of training data to improve the training efficiency.”