3 citations · 4 across the 4 of their papers we have counts for
7 papers
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…