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Found 8 papers, 1 papers with code
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
Different categories of visual stimuli activate different responses in the human brain.
MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease
Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms.
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network.
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks
The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task.
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study
Conventional PN attributes equal importance to all samples and generates prototypes by simply averaging the support sample embeddings belonging to each class.
IPNET:Influential Prototypical Networks for Few Shot Learning
Further, the influence factor of a sample is measured using MMD based on the shift in the distribution in the absence of that sample.
Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging
These experimental results demonstrate that knowledge diversification in a combined KD and ML framework outperforms conventional KD or ML techniques (with similar network configuration) that only use predictions with an average improvement of 2%.
Understanding Calibration of Deep Neural Networks for Medical Image Classification
We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes.
