21 citations · 21 across the 3 of their papers we have counts for
4 papers
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
Antoine Sanner, Camila Gonzalez, Anirban Mukhopadhyay
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distr…
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Marius Memmel, Camila Gonzalez, Anirban Mukhopadhyay
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in se…
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Andreas Bucher +3
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models a…
M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
Karol Gotkowski, Camila Gonzalez, Andreas Bucher +1
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans. The attention maps can b…