713 citations · 1.1k across the 19 of their papers we have counts for
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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…
Human Motion Analysis with Deep Metric Learning
Huseyin Coskun, David Joseph Tan, Sailesh Conjeti +2
Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identi- fication and action retrieval. N…
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Santiago Estrada, Sailesh Conjeti, Muneer Ahmad +2
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance…