Search Results for author:
Found 4 papers, 0 papers with code
Deep Prompt Tuning for Graph Transformers
We propose a novel approach called deep graph prompt tuning as an alternative to fine-tuning for leveraging large graph transformer models in downstream graph based prediction tasks.
Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers
This work highlights the potential of deep learning techniques, specifically transformer-based models, in revolutionizing the healthcare industry's approach to postoperative care.
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Especially in studies of brain diseases, research cohorts may include both neuroimaging data and genetic data, but for practical clinical diagnosis, we often need to make disease predictions only based on neuroimages.
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
