arXiv 2024

What can natural language processing do for peer review?

Ilia Kuznetsov, Osama Mahammed Afzal, Koen Dercksen, Nils Dycke, Alexander Goldberg, Tom Hope, Dirk Hovy, Jonathan K. Kummerfeld, Anne Lauscher, Kevin Leyton-Brown, Sheng Lu, Mausam, Margot Mieskes, Aurelie Neveol, Danish Pruthi, Lizhen Qu, Roy Schwartz, Noah A. Smith, Thamar Solorio, Jingyan Wang, Xiaodan Zhu, Anna Rogers, Nihar B. Shah, Iryna Gurevych

This paper was a result of the Dagstuhl seminar on peer review held in 2024. It provides an overview of the current state of peer review and where NLP techniques might interfere to improve the process.

ECIR 2023

SimpleRad: patient-friendly Dutch radiology reports

K. Dercksen, A.P. de Vries, B. van Ginneken

SimpleRad is a tool for entity linking, summarization and finding prevalence estimation in Dutch radiology reports.

SIGIR 2020

REL: an entity linker standing on the shoulders of giants

J.M. van Hulst, F. Hasibi, K. Dercksen, K. Balog, A. P. de Vries

REL (Radboud Entity Linker) is a modular entity linking framework based on state-of-the-art neural components. Code is available for local deployment. A web API is available as well.

CLEF 2020

Named entity recognition and linking on historical newspapers: UvA. ILPS & REL at CLEF HIPE 2020

V. Provatorova, S. Vakulenko, E. Kanoulas, K. Dercksen, J.M. van Hulst

Paper describing our submission to the CLEF HIPE 2020 shared task on identifying named entities in multi-lingual historical newspapers in French, German and English.

SIIRH 2020

First steps towards patient-friendly presentation of Dutch radiology reports

K. Dercksen, A.P. de Vries

Paper describing our initial efforts towards patient-friendly Dutch radiology reports. Primarily on entity linking annotation and future work.

arXiv 2020

Exploring task-based query expansion at the TREC-COVID track

T. Schoegje, C. Kamphuis, K. Dercksen, D. Hiemstra, T. Pieters, A. P. de Vries

Exploring how to generate effective queries based on search tasks for the TREC-COVID track.

IWBI 2020

Robust multi-vendor breast region segmentation using deep learning

K. Dercksen, M. Kallenberg, J. Kroes

This paper investigates a robust multi-vendor breast region segmentation system for full field digital mammograms and digital breast tomography using a U-Net neural network.

MIDL 2019

Dealing with label scarcity in computational pathology: a use case in prostate cancer classification

K. Dercksen, W. Bulten, G. Litjens

This paper investigates the performance of unsupervised and supervised deep learning methods for prostate cancer classification when few labelled data are available.