Publications (20)
Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?
Melanie Mathys, Marco Willi, Michael Graber +1
The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report d…
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Raphael Meier, Urspeter Knecht, Alain Jungo +2
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs). The presented approach, called Perturb-and-MPM, enables efficient, ap…
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Richard McKinley, Michael Rebsamen, Raphael Meier +3
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of…
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Sergio Pereira, Raphael Meier, Victor Alves +2
Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into high…
A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
Klim Kireev, Ana-Maria Creţu, Raphael Meier +3
Platforms and the law regulate digital content depicting minors (defined as individuals under 18 years of age) differently from other types of content. Given the sheer amount of co…
Combining unsupervised and supervised learning for predicting the final stroke lesion
Adriano Pinto, Sérgio Pereira, Raphael Meier +4
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-…
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
Sparse vs Contiguous Adversarial Pixel Perturbations in Multimodal Models: An Empirical Analysis
Cristian-Alexandru Botocan, Raphael Meier, Ljiljana Dolamic
Assessing the robustness of multimodal models against adversarial examples is an important aspect for the safety of its users. We craft L0-norm perturbation attacks on the preproce…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Threats and Opportunities in AI-generated Images for Armed Forces
Raphael Meier
Images of war are almost as old as war itself. From cave paintings to photographs of mobile devices on social media, humans always had the urge to capture particularly important ev…
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Adriano Pinto, Sergio Pereira, Raphael Meier +4
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularizat…
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.…
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Andreas Hess, Raphael Meier, Johannes Kaesmacher +5
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The propo…
Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
Melanie Mathys, Marco Willi, Raphael Meier
Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspi…
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
Richard McKinley, Micheal Rebsamen, Katrin Daetwyler +3
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by…
Social Media Influence Operations
Raphael Meier
Social media platforms enable largely unrestricted many-to-many communication. In times of crisis, they offer a space for collective sense-making and gave rise to new social phenom…
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Alain Jungo, Raphael Meier, Ekin Ermis +2
Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlyi…
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Alain Jungo, Raphael Meier, Ekin Ermis +4
Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, r…
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation
Raphael Meier, Michael Rebsamen, Urspeter Knecht +3
Deep learning methods for brain tumor segmentation are typically trained in an ad hoc fashion on all available data. Brain tumors are tremendously heterogeneous in image appearance…
Evaluating Concept Filtering Defenses against Child Sexual Abuse Material Generation by Text-to-Image Models
Ana-Maria Cretu, Klim Kireev, Amro Abdalla +5
We evaluate the effectiveness of filtering child images from training datasets of text-to-image models to prevent model misuse to create child sexual abuse material (CSAM). First,…