fahadshamshad / awesome-transformers-in-medical-imaging Goto Github PK

Hi,

Great work and very helpful repository.

Please, consider adding our work accepted at MIDL 2022 that extended the Vision Transformers for any genus-zero surface, and specifically to study the cortical surface in neonatal MRI scans.
https://arxiv.org/abs/2203.16414

Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
Simon Dahan, Abdulah Fawaz, Logan Z. J. Williams, Chunhui Yang, Timothy S. Coalson, Matthew F. Glasser, A. David Edwards, Daniel Rueckert, Emma C. Robinson

Our code is also available at:
https://github.com/metrics-lab/surface-vision-transformers

Thanks

Thank you for sharing this collection of papers

I also made a paper collection list about vision attention and transformer:
https://github.com/cmhungsteve/Awesome-Transformer-Attention

Feel free to check and share it!

I will also be appreciative if you can add a link to my repo.
Thank you

Hi @fahadshamshad ,

Thanks for the amazing job! I would like to ask if you could add our up-to-date survey paper on clinical report generation named "A Systematic Review of Deep Learning-based Research on Radiology Report Generation", whose GitHub repo is available here. I have proposed a PR and it would be highly appreciated if you could accept it.

Best regards,
Chang

Please, consider adding our anomaly detection study with transformers to the list.

Unsupervised Brain Anomaly Detection and Segmentation with Transformers [23 Feb 2021]
Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
https://arxiv.org/abs/2102.11650

Hi,

First of all, thank you for your great contribution to our community!
Please consider adding our work on medical transformer-based object detection:

(1) 'Focused Decoding Enables 3D Anatomical Detection by Transformers', which has been recently accepted at MELBA recently (https://www.melba-journal.org/papers/2023:003.html).

(2) 'SwinFPN: Leveraging Vision Transformers for 3D Organs-At-Risk Detection', which has been accepted as a short paper to MIDL22 (https://openreview.net/forum?id=yiIz7DhgRU5).

Code for both papers is publicly available at https://github.com/bwittmann/transoar.

Thanks in advance!