Image-level supervision and self-training for transformer-based cross-modality tumor segmentation-Reference-Cited by-同舟云学术

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Image-level supervision and self-training for transformer-based cross-modality tumor segmentation

Published:2024-10 Issue: Volume:97 Page:103287
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

de Boisredon d’Assier Malo Alefsen,Portafaix Aloys^ORCID,Vorontsov Eugene^ORCID,Le William Trung^ORCID,Kadoury Samuel^ORCID

Funder

FRQS

Canada Research Chairs

Compute Canada

NSERC

Publisher

Elsevier BV

Reference70 articles.

1. An image is worth 16x16 words: Transformers for image recognition at scale;Alexey,2021

2. Advanced normalization tools (ANTS);Avants;Insight J.,2009

3. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features;Bakas;Sci. Data,2017

4. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge;Bakas,2018

5. Source-free domain adaptation for image segmentation;Bateson;Med. Image Anal.,2022

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