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Tim Lenz

Showing results (11-20 of 18) with videos related to

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Biorxiv : the Preprint Server for Biology|January 20, 2025
Deep Learning for Biomarker Discovery in Cancer GenomesMichaela Unger, Chiara M L Loeffler, Laura Žigutytė, et al.
JHEP Reports : Innovation in Hepatology|July 17, 2025
Deep learning can predict cardiovascular events from liver imagingGregory Patrick Veldhuizen, Tim Lenz, Didem Cifci, et al.
Nature Protocols|September 16, 2024
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathologyOmar S M El Nahhas, Marko van Treeck, Georg Wölflein, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Cancer Research|June 17, 2026
Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in PathologyLaura Žigutytė, Tim Lenz, Tianyu Han, et al.
The Journal of Pathology|February 20, 2026
Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug responseNic G Reitsam, Xiaofeng Jiang, Junhao Liang, et al.
Nature Communications|July 1, 2026
A deep learning framework for efficient pathology image analysisPeter Neidlinger, Tim Lenz, Sebastian Foersch, et al.
Cancer Cell|June 11, 2026
Spatial biomarker discovery via interpretable semantic learning in histopathologyJunhao Liang, Xiaofeng Jiang, Nic Gabriel Reitsam, et al.
Pageof 2

Showing results (11-20 of 18) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 18 results.
Biorxiv : the Preprint Server for Biology|January 20, 2025
Deep Learning for Biomarker Discovery in Cancer GenomesMichaela Unger, Chiara M L Loeffler, Laura Žigutytė, et al.
JHEP Reports : Innovation in Hepatology|July 17, 2025
Deep learning can predict cardiovascular events from liver imagingGregory Patrick Veldhuizen, Tim Lenz, Didem Cifci, et al.
Nature Protocols|September 16, 2024
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathologyOmar S M El Nahhas, Marko van Treeck, Georg Wölflein, et al.
Nature Biomedical Engineering|October 1, 2025
Benchmarking foundation models as feature extractors for weakly supervised computational pathologyPeter Neidlinger, Omar S M El Nahhas, Hannah Sophie Muti, et al.
Cancer Research|June 17, 2026
Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in PathologyLaura Žigutytė, Tim Lenz, Tianyu Han, et al.
The Journal of Pathology|February 20, 2026
Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug responseNic G Reitsam, Xiaofeng Jiang, Junhao Liang, et al.
Nature Communications|July 1, 2026
A deep learning framework for efficient pathology image analysisPeter Neidlinger, Tim Lenz, Sebastian Foersch, et al.
Cancer Cell|June 11, 2026
Spatial biomarker discovery via interpretable semantic learning in histopathologyJunhao Liang, Xiaofeng Jiang, Nic Gabriel Reitsam, et al.
Pageof 2