Search Results for author:
Found 11 papers, 6 papers with code
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines.
Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs).
Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction.
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
Therefore, we report results of evaluating common voxel-wise uncertainty measures with respect to their reliability, and limitations on two medical image segmentation datasets.
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown promising results.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model.
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e. g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is crucial.
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs).
