no code implementations • 2 Sep 2023 • Tom van Sonsbeek, XianTong Zhen, Marcel Worring
We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification.
1 code implementation • 10 Mar 2023 • Tom van Sonsbeek, Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek, Marcel Worring
Most existing methods approach it as a multi-class classification problem, which restricts the outcome to a predefined closed-set of curated answers.
Ranked #1 on Medical Visual Question Answering on OVQA
no code implementations • 22 Feb 2023 • Tom van Sonsbeek, Marcel Worring
In this paper we mimic this ability by using multi-modal retrieval augmentation and apply it to several tasks in chest X-ray analysis.
no code implementations • 13 Oct 2022 • Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring
This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
1 code implementation • 12 Apr 2022 • Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring, Cees G. M. Snoek
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
no code implementations • 19 Mar 2021 • Tom van Sonsbeek, XianTong Zhen, Marcel Worring, Ling Shao
It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis.
no code implementations • 8 May 2020 • 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.