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Cross-Modal-Segmentation
trustworthy-ai/Cross-Modal-Segmentation
Cross-Modal-Segmentation is a machine learning model from trustworthy-ai. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This repository contains the checkpoints of several disentangled representation learning models for cross-modal medical image segmentation, used in the paper: 'Enhancing Cross-Modal Medical Image Segmentation through…
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Updated Jul 23, 2024
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- Access
- Public
- Created
- Jul 22, 2024
- Updated
- Jul 23, 2024
- SHA
- efecb6ad
Model card
From the Hugging Face model README
Enhancing Cross-Modal Medical Image Segmentation through Compositionality and Disentanglement
This repository contains the checkpoints of several disentangled representation learning models for cross-modal medical image segmentation, used in the paper: 'Enhancing Cross-Modal Medical Image Segmentation through Compositionality'. In particular, it contains the checkpoints of our proposed method, where we introduced compositionality into a cross-modal segmentation framework to enhance performance and interpretability, while reducing computational costs.
The checkpoints are trained for MYO, LV, and RV segmentation using the MMWHS dataset in both directions, i.e. with CT and MRI as target domain. Moreover, they are trained for liver parenchyma segmentation using the CHAOS dataset, with both MRI T1 and MRI T2 as target domain.
Please refer to the original GitHub repository for the code.
- Created
- Jul 22, 2024
- Updated
- Jul 23, 2024