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Found 4 papers, 2 papers with code
1D-Convolutional transformer for Parkinson disease diagnosis from gait
Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset.
Automating lichen monitoring in ecological studies using instance segmentation of time-lapse images
These cameras are used by ecologists in Newfoundland and Labrador to subsequently analyze and manually segment the images to determine lichen thalli condition and change.
HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait
Our hybrid architecture exploits the strengths of both Convolutional Neural Networks (ConvNets) and Transformers to accurately detect PD and determine the severity stage.
Hybrid BYOL-ViT: Efficient approach to deal with small datasets
Supervised learning can learn large representational spaces, which are crucial for handling difficult learning tasks.
