Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Using SVM and RF Time Series
By Mehdi Moradi, Purang Mousavi, Alexander H. Boag, Eric Sauerbrei, Robert Siemens, Purang Abolmaesumi. 2009
95 citations recorded by the source corpus.
Subfields: 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 Computer Vision and Pattern Recognition
- Nassir Navab
- Fausto Milletarì
- Ahmed Hosny
- Hugo J. W. L. Aerts
- Maryellen Lissak Giger
- Seyed-Ahmad Ahmadi
- Alireza Mehrtash
- Chunhua Shen
In Radiology, Nuclear Medicine and Imaging
- Hugo J. W. L. Aerts
- Ahmed Hosny
- Maryellen Lissak Giger
- Nassir Navab
- Charles Swanton
- Robert James Gillies
- Philippe Lambin
- Meghavi Rana
Citations in the corpus
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2016, methodology): “Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation [13], computer assisted diagnosis [15], interventions [24] and extraction of quantitative indices from images [1].”
- Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, background): “Here again, both classic machine learning approaches 198,199 and deep learning 200 have been used to train classifiers to grade prostate cancer in temporal ultrasound data.”