Big Data and machine learning in radiation oncology: State of the art and future prospects.

By Jean-Emmanuel Bibault, Philippe Giraud, A. Burgun. Cancer Letters, 2016

307 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/50684a8477659fac012fb0c91500680c7af21b74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

1. Ronald M. Summers
2. Xiaosong Wang
3. Kenji Suzuki

In Radiation

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

In Radiology, Nuclear Medicine and Imaging

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

Citations in the corpus

• Deep learning in medical imaging and radiation therapy. (2018, background): “The presence of electronic health records (EHR) yields the potential to collect both imaging and clinical/pathology data in order to input to DL to predict diagnosis, outcome, and guide treatments within a clinical workflow.(332) Dai et al.”