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Unsupervised Segmentation of Colonoscopy Images
Colonoscopy plays a crucial role in the diagnosis and prognosis of various gastrointestinal diseases.
Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks
The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimers disease, to the point that they can be used to train a discriminative model for disease classification.
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
In this work, we combine 12, 207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data.
Detect, Quantify, and Incorporate Dataset Bias: A Neuroimaging Analysis on 12,207 Individuals
Next, we introduce metrics to quantify the compatibility across datasets and to create embeddings of neuroimaging sites.
Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality
Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.
