Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review
By Guillaume Lemaitre, Robert Marti, Jordi Freixenet, Joan Carles Vilanova, Paul Michael Walker, Fabrice Mériaudeau. Comput. Biol. Medicine, 2015
368 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/a2eefe0b6457fc6dd62253c9dac86b0bbdd5a8ee
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In Artificial Intelligence
In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2021, methodology): “We further collect prostate T2-weighted MRI images from 6 different data sources partitioned from the public datasets [2, 21, 31, 33] for prostate MRI segmentation task.”
- MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020, background): “sites [45], [46], which presents visible heterogeneity and is similar to the real clinical circumstance.”
- Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images (2017, positive): “In particular, computerized image processing technology has been shown to improve performance, correctness, and robustness in histopathology assessments (Lemaître et al., 2015).”