38 citations · 81 across the 13 of their papers we have counts for
14 papers
Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI
Julia Huang, Camila Gonzalez, Rydham Goyal +5
For patients with Moyamoya disease, impaired cerebrovascular reserve (CVR) is an important hemodynamic criterion for recommending extracranial-to-intracranial bypass surgery. Stand…
BM-CL: Bias Mitigation through the lens of Continual Learning
Lucas Mansilla, Rodrigo Echeveste, Camila Gonzalez +2
Biases in machine learning pose significant challenges, particularly when models amplify disparities that affect disadvantaged groups. Traditional bias mitigation techniques often…
Continual Learning in Medical Imaging: A Survey and Practical Analysis
Mohammad Areeb Qazi, Anees Ur Rehman Hashmi, Santosh Sanjeev +4
Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distr…
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez +3
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context o…
Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning
Niklas Babendererde, Moritz Fuchs, Camila Gonzalez +2
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catast…
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Moritz Fuchs +5
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high res…
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Camila Gonzalez, Amin Ranem, Ahmed Othman +1
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explo…
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem +5
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fas…
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…
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı +39
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms,…
What is Wrong with Continual Learning in Medical Image Segmentation?
Camila Gonzalez, Nick Lemke, Georgios Sakas +1
Continual learning protocols are attracting increasing attention from the medical imaging community. In continual environments, datasets acquired under different conditions arrive…
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…