A deep learning framework for supporting the classification of breast lesions in ultrasound images
By Seokmin Han, Hokyung Kang, J. Jeong, Moon Ho Park, Wonsik Kim, Won-Chul Bang, Yeong Kyeong Seong. Physics in Medicine and Biology, 2017
337 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/216cc6fccb3e239350564213d36830d3ca2bbc28
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
In Computer Vision and Pattern Recognition
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
- Xin Yang
Citations in the corpus
- Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “…Bardou et al. 2018; Bayramoglu et al. 2017; Bejnordi et al. 2017b; Cao et al. 2016; Chang et al. 2017; Feng et al. 2018; Gandomkar et al. 2018; Han et al. 2017b; Murtaza et al. 2019; Nahid and Kong 2017b, 2018; Nahid et al. 2018; Nejad et al. 2017; Spanhol et al. 2016a, 2017; Wan et al. 2017;…”
- Deep Learning in Medical Ultrasound Analysis: A Review (2019, methodology): “…the stacked denoising auto-encoder (SDAE) [88], a combination of the point-wise gated Boltzmann machine (PGBM) and RBM [89], and the GoogLeNet CNN [90] have been applied to breast US or shear-wave elastography images for breast cancer diagnosis; all of these obtained a superior performance when…”