Search Results for author: Nazanin Mohammadi Sepahvand

Found 2 papers, 1 papers with code

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images

1 code implementation30 Mar 2021 Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark, Tal Arbel

The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET).

Knowledge Distillation Segmentation +1

Exploring Bayesian Deep Learning Uncertainty Measures for Segmentation of New Lesions in Longitudinal MRIs

no code implementations MIDL 2019 Nazanin Mohammadi Sepahvand, Raghav Mehta, Douglas Lorne Arnold, Doina Precup, Tal Arbel

In this paper, we develop a modified U-Net architecture to accurately segment new and enlarging lesions in longitudinal MRI, based on multi-modal MRI inputs, as well as subtrac- tion images between timepoints, in the context of large-scale clinical trial data for patients with Multiple Sclerosis (MS).

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