367 citations · 581 across the 7 of their papers we have counts for
7 papers
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann +1
Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex mach…
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +2
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and varia…
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult fo…
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Abhijit Guha Roy, Sailesh Conjeti, Debdoot Sheet +3
Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data ar…
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
Benjamín Gutiérrez, Loïc Peter, Tassilo Klein +1
With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply inc…
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learn…