What Your Radiologist Might be Missing: Using Machine Learning to Identify Mislabeled Instances of X-ray Images. - Tim Rädsch; Sven Eckhardt; Florian Leiser; Konstantin D. Pandl; Scott Thiebes; Ali Sunyaev (HICSS, 2021)

Towards a Machine Learning-based Decision Support System for Dispatching Helicopters in New Zealand. - Tim Rädsch; Melanie Reuter-Oppermann; Dave Richards (HICSS, 2021)

Common Limitations of Image Processing Metrics: A Picture Story. - Annika Reinke; Matthias Eisenmann; Minu Dietlinde Tizabi; Carole H. Sudre; Tim Rädsch; Michela Antonelli; Tal Arbel; Spyridon Bakas; M. Jorge Cardoso; Veronika Cheplygina; Keyvan Farahani; Ben Glocker; et al. (CoRR, 2021)

Metrics reloaded: Pitfalls and recommendations for image analysis validation. - Lena Maier-Hein; Annika Reinke; Evangelia Christodoulou; Ben Glocker; Patrick Godau; Fabian Isensee; Jens Kleesiek; Michal Kozubek 001; Mauricio Reyes 001; Michael A. Riegler; Manuel Wiesenfarth; Michael Baumgartner 001; et al. (CoRR, 2022)

Labeling instructions matter in biomedical image analysis. - Tim Rädsch; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Nicholas Schreck; A. Emre Kavur; Bünyamin Pekdemir; Tobias Roß; Annette Kopp-Schneider; Lena Maier-Hein (CoRR, 2022)

Why is the Winner the Best? - Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; et al. (CVPR, 2023)

Understanding metric-related pitfalls in image analysis validation. - Annika Reinke; Minu Tizabi; Michael Baumgartner 001; Matthias Eisenmann; Doreen Heckmann-Nötzel; A. Emre Kavur; Tim Rädsch; Carole H. Sudre; Laura Ación; Michela Antonelli; Tal Arbel; Spyridon Bakas; et al. (CoRR, 2023)

Why is the winner the best? - Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; et al. (CoRR, 2023)

Labeling instructions matter in biomedical image analysis. - Tim Rädsch; Lena Maier-Hein (Zenodo, 2023)

Labelling instructions matter in biomedical image analysis. - Tim Rädsch; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Nicholas Schreck; A. Emre Kavur; Bünyamin Pekdemir; Tobias Roß; Annette Kopp-Schneider; Lena Maier-Hein (Nat. Mac. Intell., 2023)

Quality Assured: Rethinking Annotation Strategies in Imaging AI. - Tim Rädsch; Annika Reinke; Vivienn Weru; Minu Dietlinde Tizabi; Nicholas Heller; Fabian Isensee; Annette Kopp-Schneider; Lena Maier-Hein (ECCV (78), 2024)

Metrics Reloaded: A framework for trustworthy image analysis validation. - Ali Emre Kavur; Annika Reinke; Paul F. Jäger; Fabian Isensee; Tim Rädsch; Minu Dietlinde Tizabi; Lena Maier-Hein (Zenodo, 2024)

In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. - Amelia Jiménez-Sánchez; Natalia Rozalia Avlona; Sarah de Boer; Víctor M. Campello; Aasa Feragen; Enzo Ferrante; Melanie Ganz; Judy Wawira Gichoya; Camila González; Steff Groefsema; Alessa Hering; Adam Hulman; et al. (FAccT, 2025)

Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation. - Tim Rädsch; Leon D. Mayer; Simon Pavicic; A. Emre Kavur; Marcel Knopp; Baris Öztürk; Klaus H. Maier-Hein; Paul F. Jaeger; Fabian Isensee; Annika Reinke; Lena Maier-Hein (CoRR, 2025)

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study. - Leon D. Mayer; Tim Rädsch; Dominik Michael; Lucas Luttner; Amine Yamlahi; Evangelia Christodoulou; Patrick Godau; Marcel Knopp; Annika Reinke; Fiona R. Kolbinger; Lena Maier-Hein (CoRR, 2025)