Search Results for author:
Found 7 papers, 4 papers with code
vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images
This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations.
Negligible effect of brain MRI data preprocessing for tumor segmentation
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.
Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation
We analyze a dataset of paired CT images, where smooth and sharp images were reconstructed from the same sinograms with different kernels, thus providing identical anatomy but different style.
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
We propose a loss reweighting approach to increase the ability of the network to detect small lesions.
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital.
Connectivity-Driven Brain Parcellation via Consensus Clustering
We present two related methods for deriving connectivity-based brain atlases from individual connectomes.
Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context.
