11 citations · 22 across the 7 of their papers we have counts for
7 papers
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports
Tom van Sonsbeek, Xiantong Zhen, Marcel Worring
The way we analyse clinical texts has undergone major changes over the last years. The introduction of language models such as BERT led to adaptations for the (bio)medical domain l…
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models
Tom van Sonsbeek, Mohammad Mahdi Derakhshani, Ivona Najdenkoska +2
Medical Visual Question Answering (VQA) is an important challenge, as it would lead to faster and more accurate diagnoses and treatment decisions. Most existing methods approach it…
X-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation
Tom van Sonsbeek, Marcel Worring
An important component of human analysis of medical images and their context is the ability to relate newly seen things to related instances in our memory. In this paper we mimic t…
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra +1
Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods…
LifeLonger: A Benchmark for Continual Disease Classification
Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek +4
Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing new…
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
Tom van Sonsbeek, Xiantong Zhen, Marcel Worring +1
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electr…
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning
Tom van Sonsbeek, Veronika Cheplygina
Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures. With the increased numbers of deep learni…