Search Results for author:
Found 16 papers, 9 papers with code
Effect of latent space distribution on the segmentation of images with multiple annotations
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Future Unruptured Intracranial Aneurysm Growth Prediction using Mesh Convolutional Neural Networks
The model consisted of a mesh convolutional neural network including additional novel input edge features of shape index and curvedness which describe the surface topology.
Generalized Probabilistic U-Net for medical image segementation
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods.
Progressive GANomaly: Anomaly detection with progressively growing GANs
The method is tested using Fashion MNIST, Medical Out-of-Distribution Analysis Challenge (MOOD), and in-house brain MRI; using patches of sizes 16x16 and 32x32.
MixLacune: Segmentation of lacunes of presumed vascular origin
Lacunes of presumed vascular origin are fluid-filled cavities of between 3 - 15 mm in diameter, visible on T1 and FLAIR brain MRI.
MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds
Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects.
MixMicrobleedNet: segmentation of cerebral microbleeds using nnU-Net
Final evaluation on the test set of the VALDO challenge is pending.
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs
The L2-optimized VAE outperforms SSIM, with improved reconstruction metrics and DSIs for both datasets.
Using uncertainty estimation to reduce false positives in liver lesion detection
We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
Liver segmentation and metastases detection in MR images using convolutional neural networks
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation.
