Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches
By Guy Amit, Rami Ben-Ari, Omer Hadad, Einat Monovich, Noa Granot, Sharbell Y. Hashoul. 2017
51 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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
- Brett K. Beaulieu-Jones
- Yifan Peng
- Anshul B Kundaje
- Avanti Shrikumar
- Casey S. Greene
- Amr M. Alexandari
- Zhiyong Lu
- S. Joshua Swamidass
In Radiology, Nuclear Medicine and Imaging
- Mohammed Ali Al-Garadi
- Ainuddin Wahid Abdul Wahab
- Ghulam Murtaza
- Ghulam Raza
- Fariha Zulfiqar
- Ghulam Mujtaba
- Henry Friday Nweke
- Alexandr A Kalinin
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[56] investigated the tradeoff between pre-trained models from a different domain and a small CNN trained only with MRI images.”
- Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “However, very few studies used MRI to classify BrC (Amit et al. 2017; Bevilacqua et al. 2016; Rasti et al. 2017) possibly because of the unavailability of public datasets.”