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pro vyhledávání: '"van Sonsbeek, Tom"'
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 like PubMedBERT and ClinicalBERT. These models rely on large databases
Externí odkaz:
http://arxiv.org/abs/2309.00917
Autor:
van Sonsbeek, Tom, Derakhshani, Mohammad Mahdi, Najdenkoska, Ivona, Snoek, Cees G. M., Worring, Marcel
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 as a multi-class classification problem, which restricts the outcome
Externí odkaz:
http://arxiv.org/abs/2303.05977
Autor:
van Sonsbeek, Tom, Worring, Marcel
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 this ability by using multi-modal retrieval augmentation and apply it t
Externí odkaz:
http://arxiv.org/abs/2302.11352
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 that have made a large impact on the vision field increasingly more d
Externí odkaz:
http://arxiv.org/abs/2210.06980
Autor:
Derakhshani, Mohammad Mahdi, Najdenkoska, Ivona, van Sonsbeek, Tom, Zhen, Xiantong, Mahapatra, Dwarikanath, Worring, Marcel, Snoek, Cees G. M.
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 newly annotated medical data from different sources. To exploit the incom
Externí odkaz:
http://arxiv.org/abs/2204.05737
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electronic Health Records (EHRs), which contain rich information on patients
Externí odkaz:
http://arxiv.org/abs/2103.10825
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 learning publications and openly available code, the approach to choosing a
Externí odkaz:
http://arxiv.org/abs/2005.08869
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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 that have made a large impact on the vision field increasingly more d