Search Results for author:
Found 5 papers, 5 papers with code
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data
To address these issues, we propose Triplet Training for differential diagnosis with limited target data.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity.
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications.
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data.
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional image information for classifying patients.
