155 citations · 286 across the 5 of their papers we have counts for
5 papers
HyperSORT: Self-Organising Robust Training with hyper-networks
Samuel Joutard, Marijn Stollenga, Marc Balle Sanchez +2
Medical imaging datasets often contain heterogeneous biases ranging from erroneous labels to inconsistent labeling styles. Such biases can negatively impact deep segmentation netwo…
Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks
Vincent Bürgin, Raphael Prevost, Marijn F. Stollenga
Automatic vertebra localization and identification in CT scans is important for numerous clinical applications. Much progress has been made on this topic, but it mostly targets pos…
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation
Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga +4
Depth estimation from monocular images is an important task in localization and 3D reconstruction pipelines for bronchoscopic navigation. Various supervised and self-supervised dee…
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki +1
Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of…
Deep Networks with Internal Selective Attention through Feedback Connections
Marijn Stollenga, Jonathan Masci, Faustino Gomez +1
Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers.…