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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.

Diff-SCM

Code for Diff-SCM paper

Python

DiffAN

Diffusion Models for Causal Discovery

Python

CSDisentanglement_Metrics_Library

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.

Python

vMFNet

vMFNet: Compositionality Meets Domain-generalised Segmentation

Python

cardiac_dataset_trasformer

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.

Python

CMRxRECON_Challenge_EDIPO

Inference code for EDIPO group participating in CMRxRECON challenge by MICCAI

Python

Inference_Denoising_MRI_Recon

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

Python

vMFNet-Journal

Python

ISBI-2024-DiMEDIA-MONAI-Tutorial

Jupyter Notebook

multiscale-adversarial-attention-gates

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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vios-s/disentanglement_tutorial

vios-s

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