scholarly journals A deep learning model for preoperative differentiation between glioblastoma, central nervous system lymphoma and brain metastasis

2021 ◽  
Vol 1 ◽  
pp. 100828
Author(s):  
L. Tariciotti ◽  
V.M. Caccavella ◽  
G. Fiore ◽  
G.A. Bertani ◽  
G. Carrabba ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Brain Metastasis ◽  
Central Nervous System Lymphoma ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning

2020 ◽  
Vol 53 (1) ◽  
pp. 259-268 ◽  
Author(s):  
Lenhard Pennig ◽  
Ulrike Cornelia Isabel Hoyer ◽  
Lukas Goertz ◽  
Rahil Shahzad ◽  
Thorsten Persigehl ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Clinical Evaluation ◽  
Central Nervous System Lymphoma ◽  
Multiparametric Mri ◽  
Automated Segmentation

scholarly journals NIMG-20. ANALYSIS OF PERFUSION CT PARAMETERS FOR DIFFERENTIATING AMONG GLIOBLASTOMA, PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA AND BRAIN METASTASIS

2017 ◽  
Vol 19 (suppl_6) ◽  
pp. vi146-vi146 ◽  
Author(s):  
Shumpei Onishi ◽  
Yoshinori Kajiwara ◽  
Takeshi Takayasu ◽  
Manish Kolakshyapati ◽  
Minoru Ishifuro ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Brain Metastasis ◽  
Central Nervous System Lymphoma ◽  
Perfusion Ct

scholarly journals Learning to leverage salient regions in neuro-oncology using Deep Learning

2020 ◽  
Author(s):  
A. Grigis ◽  
A. Alentorn ◽  
V. Frouin
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Brain Tumors ◽  
Computational Cost ◽  
Central Nervous System Lymphoma ◽  
Tumor Tissues ◽  
Anomaly Detector ◽  
Salient Regions ◽  
Performance Results

AbstractDeveloping an automatic tumor detector for MRI medical images is a major challenge in neuro-oncology. The availability of such a tool would be a valuable assistance for the radiologists. Numerous works have tried to segment the tumor tissues, others have attempted to localize the tumor globally. In this work we focus on this second class of methods and we compare two drastically different strategies. The first one is an assumption-free anomaly detector build over a Variational Auto-Encoder (VAE), and the second one is a VGG classifier that embed Attention-Gated (AG) units to focus on the target structures at almost no additional computational cost. This comparison is first conducted on the publicly available BraTS glioma dataset for which published performance results can serve as reference, and extended as such (ie., without transfer learning) to two internal image datasets, namely Primary Central Nervous System Lymphoma (PCNSL) and Metastasis. The results demonstrate that the VAE and AG-VGG strategies can be used, up to a certain extent, to localize brain tumors.

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