72 citations · 238 across the 12 of their papers we have counts for
12 papers
Using Synthetic Images to Augment Small Medical Image Datasets
Minh H. Vu, Lorenzo Tronchin, Tufve Nyholm +1
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require very large labeled datasets. Ho…
Reproducibility of the Methods in Medical Imaging with Deep Learning
Attila Simko, Anders Garpebring, Joakim Jonsson +2
Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be i…
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
Sandeep Kaushik, Mikael Bylund, Cristina Cozzini +12
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular fo…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation
Minh H. Vu, Gabriella Norman, Tufve Nyholm +1
In the last years, deep learning has dramatically improved the performances in a variety of medical image analysis applications. Among different types of deep learning models, conv…
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation
Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice. Unfortunately, manual segmentation is very challenging, time-cons…
A Question-Centric Model for Visual Question Answering in Medical Imaging
Minh H. Vu, Tommy Löfstedt, Tufve Nyholm +1
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transpare…
Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation
Minh H. Vu, Guus Grimbergen, Tufve Nyholm +1
When using Convolutional Neural Networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as sin…
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks
Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma…
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
Minh H. Vu, Guus Grimbergen, Attila Simkó +2
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within…
A Generalized Network for MRI Intensity Normalization
A. Simkó, T. Löfstedt, A. Garpebring +2
Image normalization, the correction for intra-volume inhomogeneities in magnetic resonance imaging (MRI) data has little significance for visual diagnosis, but is a crucial step be…
Whole-brain substitute CT generation using Markov random field mixture models
Anders Hildeman, David Bolin, Jonas Wallin +4
Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixtur…