I'm going to co-organize the tutorial on "Weakly Supervised CNN Segmentation: Models and Optimization" in MICCAI 2019. More details please refer to link
Our paper 'Constrained CNN-losses for weakly supervised segmentation' has been accepted for publication at MedIA journal.
I have been appointed to Assistant Professor at the department of software and IT and the Ecole de Technologie Superieure, in Montreal, starting from November,2018.
Our paper 'HyperDense-Net: A hyper-densely connected CNN for multi-modal segmentation' has been accepted for publication at IEEE Transactions on Medical Imaging link.
Our paper 'Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks' has been accepted for publication at the journal of Medical Physics link
The collation study from the ENIGMA challenge comparing methods for cerebellum parcellation on MRI has been accepted at Neuroimage link
I'll be co-chairing a session on Brain Image Segmentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 in Washington. DC, USA.
Our HyperDense network has achieved the first position at the MRBrains'13 Challenge among 47 teams.
Our paper "A 3D fully convolutional neural network and a random walker to segment the esophagus in CT" has been selected as Editor's Choice for December 2017 at Medical Physics.link
We have released the code of our SemiDenseNet CNN for the segmentation of infant brain tissue. link
I will be presenting our work to segment infant brain tissue with an ensemble of CNNs at the iSEG Grand Challenge 2017 in MICCAI'17, Quebec, Canada
Our team ranked among the top methods (1st and 2nd in most of the metrics) on the iSEG Grand MICCAI Challenge 2017 with our work: "Infant brain tissue segmentation: an ensemble of semi-dense fully CNNs approach."
We received the "Travel Student MICCAI award" for our paper "Unbiased shape compactness for segmentation"
Our latest paper "Unbiased shape compactness for segmentation" has been accepted at MICCAI 2017.
Our paper "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study" has been accepted at NeuroImage. link
The code of our paper "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study" has been released in GitHub. link
Our paper "DOPE: Distributed Optimization for Pairwise Energies" has been accepted at CVPR 2017, Honolulu.
I have been invited as speaker at the summit "deep learning in healthcare" (https://www.re-work.co/events/deep-learning-health-boston-2017), which will be held in Boston on May 25-26th, 2017. There I'll talk about Segmentation of Medical Images via Deep Learning Techniques: Current State-Of-The-Art and Perspectives.