Cardinal

> Github repository
In this paper, we explore the application of existing foundation models to cytological classification tasks, focusing on low-rank adaptation (LoRA), a parameter-efficient fine-tuning method well-suited to few-shot learning scenarios. We evaluate five foundation models across four cytological classification datasets. Our results demonstrate that fine-tuning the pre-trained backbones with LoRA significantly enhances model performance compared to merely fine-tuning the classifier head, achieving state-of-the-art results on both simple and complex classification tasks while requiring fewer data samples.

> Github repository
The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines.

BMAT is now compatible to work with remote server using shared samba folder and a slurm scheduler to process data on remote server. It has to be noted that this feature has been implemented for users based in the Institute of NeuroSciences (IoNS) from UCLouvain. Therefore, it may not work easily with every servers, but feel free to fork the code and adapt it for your institue.