8 citations · 14 across the 13 of their papers we have counts for
15 papers
DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation
Vasileios Baltatzis, Mert Inan, Connor Gillis +4
Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language comprehension. We introduce D…
Bootstrapping Sign Language Annotations with Sign Language Models
Colin Lea, Vasileios Baltatzis, Connor Gillis +3
AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters an…
Towards AI-driven Sign Language Generation with Non-manual Markers
Han Zhang, Rotem Shalev-Arkushin, Vasileios Baltatzis +7
Sign languages are essential for the Deaf and Hard-of-Hearing (DHH) community. Sign language generation systems have the potential to support communication by translating from writ…
Neural Sign Actors: A diffusion model for 3D sign language production from text
Vasileios Baltatzis, Rolandos Alexandros Potamias, Evangelos Ververas +3
Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities. Deep learning methods for SL recognition and translation have achieved…
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
Vasileios Baltatzis, Luca Costabello
Despite being the go-to choice for link prediction on knowledge graphs, research on interpretability of knowledge graph embeddings (KGE) has been relatively unexplored. We present…
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
Kyriaki-Margarita Bintsi, Tamara T. Mueller, Sophie Starck +3
The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucia…
Multimodal brain age estimation using interpretable adaptive population-graph learning
Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Rolandos Alexandros Potamias +2
Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer's. Population graphs, which inclu…
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
Sam Ellis, Octavio E. Martinez Manzanera, Vasileios Baltatzis +6
GANs are able to model accurately the distribution of complex, high-dimensional datasets, e.g. images. This makes high-quality GANs useful for unsupervised anomaly detection in med…
Is MC Dropout Bayesian?
Loic Le Folgoc, Vasileios Baltatzis, Sujal Desai +7
MC Dropout is a mainstream "free lunch" method in medical imaging for approximate Bayesian computations (ABC). Its appeal is to solve out-of-the-box the daunting task of ABC and un…
Voxel-level Importance Maps for Interpretable Brain Age Estimation
Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Alexander Hammers +1
Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative disea…
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loic Le Folgoc +6
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published resul…
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Vasileios Baltatzis, Loic Le Folgoc, Sam Ellis +6
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss…
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary +8
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as…
Patch-based Brain Age Estimation from MR Images
Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson +2
Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. This is a potential biomarker…
Bayesian Sampling Bias Correction: Training with the Right Loss Function
L. Le Folgoc, V. Baltatzis, A. Alansary +8
We derive a family of loss functions to train models in the presence of sampling bias. Examples are when the prevalence of a pathology differs from its sampling rate in the trainin…