Cargando…
Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research
Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in...
Autores principales: | Masoudi, Samira, Harmon, Stephanie A., Mehralivand, Sherif, Walker, Stephanie M., Raviprakash, Harish, Bagci, Ulas, Choyke, Peter L., Turkbey, Baris |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790158/ https://www.ncbi.nlm.nih.gov/pubmed/33426151 http://dx.doi.org/10.1117/1.JMI.8.1.010901 |
Ejemplares similares
-
Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans
por: MASOUDI, SAMIRA, et al.
Publicado: (2021) -
Harnessing clinical annotations to improve deep learning performance in prostate segmentation
por: Sarma, Karthik V., et al.
Publicado: (2021) -
Sentinel lymph node imaging in urologic oncology
por: Mehralivand, Sherif, et al.
Publicado: (2018) -
Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma
por: Patkar, Sushant, et al.
Publicado: (2023) -
A comparison of prostate cancer bone metastases on (18)F-Sodium Fluoride and Prostate Specific Membrane Antigen ((18)F-PSMA) PET/CT: Discordant uptake in the same lesion
por: Harmon, Stephanie A., et al.
Publicado: (2018)