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Project page of the paper: "Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates", by Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris (2021) IEEE TMI

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Code for Diff-SCM paper

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This repository summarizes the material gathered for the tutorial on learning disentangled representations in the imaging domain, and serves as a roadmap for the disentanglement aficionados.

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This repository constists of the implementations of the Distance Correlation (DC) and Information Over Bias (IOB) metrics proposed in [link]. The two metrics can be used to assess the level of disentanglement between spatial content and vector style representations. Both metrics are ready to use with PyTorch and TensorFlow implementations.

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This repository has been created to improve the young researchers' experience when using cardiac data for any medical imaging application. It consists of transformers that reorganize a DICOM-based dataset into traditional image dataset structure for better dataloader exploitation.

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CMRxRECON_Challenge_EDIPO

Inference code for EDIPO group participating in CMRxRECON challenge by MICCAI

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Inference_Denoising_MRI_Recon

Office code for "Inference Stage Denoising for Undersampled MRI Reconstruction"

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vMFNet-Journal

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ISBI-2024-DiMEDIA-MONAI-Tutorial

Jupyter Notebook

BMFT

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vios-s/multiscale-adversarial-attention-gates

vios-s

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