Multi-modal medical Transformers: A meta-analysis for medical image segmentation in oncology

Author:

Andrade-Miranda GustavoORCID,Jaouen Vincent,Tankyevych Olena,Cheze Le Rest Catherine,Visvikis Dimitris,Conze Pierre-Henri

Funder

Agence nationale de la recherche

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference101 articles.

1. Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text;Akbari,2021

2. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C., 2021. Vivit: A video vision transformer. In: International Conference on Computer Vision (ICCV). pp. 6836–6846.

3. The RSNA-ASNR-MICCAI braTS 2021 benchmark on brain tumor segmentation and radiogenomic classification;Baid,2021

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

5. Bao, H., Dong, L., Wei, F., 2022. BEiT: BERT Pre-Training of Image Transformers. In: International Conference on Learning Representations (ICLR).

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