65 citations · 69 across the 6 of their papers we have counts for
7 papers · 1 filter
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
Can Taylan Sari, Sila Kurugol, Onur Afacan +1
Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and e…
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K. Warfield +1
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However…
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
Seyed Raein Hashemi, Sanjay P. Prabhu, Simon K. Warfield +1
The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks. In this paper, we propose new deep learning strategies for Den…
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Recently, the deep learning-…
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
Tatsuya Yokota, Burak Erem, Seyhmus Guler +2
Let us consider a case where all of the elements in some continuous slices are missing in tensor data. In this case, the nuclear-norm and total variation regularization methods usu…
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus +3
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training su…