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Name: Laboratory for Imagery, Vision and Artificial Intelligence
Type: Organization
Location: Montréal, Canada
Blog: https://www.etsmtl.ca/Unites-de-recherche/LIVIA/accueil
Name: Laboratory for Imagery, Vision and Artificial Intelligence
Type: Organization
Location: Montréal, Canada
Blog: https://www.etsmtl.ca/Unites-de-recherche/LIVIA/accueil
Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.
Oral presentation at MIDL 2020 - Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
Code for our arxiv preprint: https://arxiv.org/abs/1904.04205
This repository aims at containing all the code employed at LIVIA to segment medical images. Mainly, our research focuses on bringind the expertise in deep learning and optimization techniques to the medical image analysis domain.
Material of the MICCAI 2020 tutorial on weakly supervised learning for semantic segmentation. The updated tutorial (2021 and onwards) is available there: https://github.com/LIVIAETS/miccai_weakly_supervised_tutorial
Code for tutorial at MICCAI 2022
Code for our arxiv preprint: https://arxiv.org/abs/1904.05236
Code of our MIDL 2018 paper and MedIA extension: https://arxiv.org/abs/1805.04628
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
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