Browsing by author "Zormpas Petridis, Konstantinos"
Now showing items 1-4 of 4
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Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI.
Zormpas-Petridis, K; Tunariu, N; Collins, DJ; Messiou, C; Koh, D-M; et al. (PERGAMON-ELSEVIER SCIENCE LTD, 2022-10-01)PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. ... -
MRI Imaging of the Hemodynamic Vasculature of Neuroblastoma Predicts Response to Antiangiogenic Treatment.
Zormpas-Petridis, K; Jerome, NP; Blackledge, MD; Carceller, F; Poon, E; et al. (AMER ASSOC CANCER RESEARCH, 2019-06)Childhood neuroblastoma is a hypervascular tumor of neural origin, for which antiangiogenic drugs are currently being evaluated; however, predictive biomarkers of treatment response, crucial for successful delivery of ... -
SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.
Zormpas-Petridis, K; Noguera, R; Ivankovic, DK; Roxanis, I; Jamin, Y; et al. (FRONTIERS MEDIA SA, 2021-01-20)High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), ... -
Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology.
Zormpas-Petridis, K; Failmezger, H; Raza, SEA; Roxanis, I; Jamin, Y; et al. (FRONTIERS MEDIA SA, 2019-10-11)Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. ...