5 citations · 9 across the 5 of their papers we have counts for
9 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…
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…