Deep Learning and Convolutional Neural Networks for Medical Image Computing

By Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin F. Yang. 2017

204 citations recorded by the source corpus.

Subfields: 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 Radiology, Nuclear Medicine and Imaging

1. Andreas K. Maier
2. Christian Riess
3. Olivier Elemento
4. Tobias Lasser
5. Christopher Syben

Citations in the corpus

• RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, methodology): “patio-temporal attention model with LSTM to automatically mine the discriminative joints and learn the respective and different attentions of each frame along the temporal axis. Similarly, Liu et al. [20] proposed a Global Context-Aware Attention LSTM (GCA-LSTM) to selectively focus on the informati”
• Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images (2017, background): “, normal versus cancerous), the image interpretation of heterogeneous populations still suffers from lack of robust computerization approaches (Razzak et al., 2017; Carneiro et al., 2017; Gurcan, 2016; Jiang et al., 2016).”
• A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “tutorials and reviews [9, 10, 11, 12, 13, 14, 15, 16, 17, 18] over blog posts and jupyter notebooks [19, 20, 21] to entire books [22, 23].”