72 citations · 185 across the 18 of their papers we have counts for
19 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…
Compressing regularized dynamics improves link prediction with the map equation in sparse networks
Maja Lindström, Christopher Blöcker, Tommy Löfstedt +1
Predicting future interactions or novel links in networks is an indispensable tool across diverse domains, including genetic research, online social networks, and recommendation sy…
Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt +1
Deploying adversarially robust machine learning systems requires continuous trade-offs between robustness, cost, and latency. We present an autonomic decision-support framework pro…
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…
Synthesizing multi-log grasp poses in cluttered environments
Arvid Fälldin, Tommy Löfstedt, Tobias Semberg +2
Multi-object grasping is a challenging task. It is important for energy and cost-efficient operation of industrial crane manipulators, such as those used to collect tree logs from…
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt +1
Machine learning models -- deep neural networks in particular -- have performed remarkably well on benchmark datasets across a wide variety of domains. However, the ease of finding…
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…
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…
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…
A general multiblock method for structured variable selection
Tommy Löfstedt, Fouad Hadj-Selem, Vincent Guillemot +5
Regularised canonical correlation analysis was recently extended to more than two sets of variables by the multiblock method Regularised generalised canonical correlation analysis…
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem +4
Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to repre…
Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
Fouad Hadj-Selem, Tommy Lofstedt, Elvis Dohmatob +4
Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in…
Predictive support recovery with TV-Elastic Net penalty and logistic regression: an application to structural MRI
Mathieu Dubois, Fouad Hadj-Selem, Tommy Lofstedt +4
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relat…