72 citations · 223 across the 12 of their papers we have counts for
12 papers
A physics-informed, plug-and-play dose engine for gradient-based radiotherapy treatment planning
Attila Simkó, Matthias Kronsteiner, Simon Glatzer +11
Radiotherapy treatment planning remains a time-intensive iterative process requiring expert intervention in commercial treatment planning system (TPS). While machine learning appro…
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…
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Minh H. Vu, Daniel Edler, Carl Wibom +3
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current…
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Lorenzo Tronchin, Minh H. Vu, Paolo Soda +1
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard D…
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…
Fast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering
Yeman B. Hagos, Vu H. Minh, Saed Khawaldeh +2
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelit…