713 citations · 1.1k across the 19 of their papers we have counts for
19 papers
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
Ivan Ezhov, Marcel Rosier, Lucas Zimmer +12
Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical them…
FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
Leonie Henschel, Sailesh Conjeti, Santiago Estrada +3
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands o…
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab +1
We introduce Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans. Next to the Bayesian fully convolutional neural network, we also prese…
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
Shubham Kumar, Sailesh Conjeti, Abhijit Guha Roy +2
We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI…
Learning Optimal Deep Projection of F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
Shubham Kumar, Abhijit Guha Roy, Ping Wu +10
Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission t…