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Found 9 papers, 3 papers with code
Is MC Dropout Bayesian?
We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
This mismatch is known as sampling bias.
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
Attention U-Net: Learning Where to Look for the Pancreas
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on
Pancreas Segmentation
on CT-150
