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Daniel Siegismund

Showing results (1-10 of 12) with videos related to

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SLAS Discovery : Advancing Life Sciences R & D|November 22, 2025
PCIM: Learning pixel attributions via pixel-wise channel isolation mixing in high content imagingDaniel Siegismund, Mario Wieser, Stephan Heyse, et al.
SLAS Technology|January 21, 2022
Benchmarking feature selection methods for compressing image information in high-content screeningDaniel Siegismund, Matthias Fassler, Stephan Heyse, et al.
Materials Science & Engineering. C, Materials for Biological Applications|November 28, 2014
Evaluation of wettability and surface energy of native Nitinol surfaces in relation to hemocompatibilitySvetlana A Shabalovskaya, Daniel Siegismund, Erik Heurich, et al.
Acta Biomaterialia|September 28, 2013
Quantification of the interaction between biomaterial surfaces and bacteria by 3-D modelingDaniel Siegismund, Andreas Undisz, Sebastian Germerodt, et al.
Macromolecular Bioscience|July 6, 2010
Fibrinogen adsorption on biomaterials--a numerical studyDaniel Siegismund, Thomas F Keller, Klaus D Jandt, et al.
Assay and Drug Development Technologies|August 28, 2018
Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown PhenotypesOliver Dürr, Elvis Murina, Daniel Siegismund, et al.
SLAS Discovery : Advancing Life Sciences R & D|February 5, 2026
AI-Based Analysis of Label-Free Live Cell Imaging of T-Cell Mediated Tumor Killing Assay Enables Competitive and Robust Hit CallingJosefa Chuh, Daniel Siegismund, John Moffat, et al.
Drug Research|January 18, 2018
Developing Deep Learning Applications for Life Science and Pharma IndustryDaniel Siegismund, Vasily Tolkachev, Stephan Heyse, et al.
SLAS Discovery : Advancing Life Sciences R & D|March 22, 2026
Response to Letter Regarding "AI‑based analysis of label‑free live‑cell imaging of T‑cell-mediated tumor killing"Josefa Chuh, Daniel Siegismund, John Moffat, et al.
SLAS Discovery : Advancing Life Sciences R & D|May 21, 2020
Deep Learning-Based HCS Image Analysis for the EnterpriseStephan Steigele, Daniel Siegismund, Matthias Fassler, et al.
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Showing results (1-10 of 12) with videos related to

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Pageof 2
SLAS Discovery : Advancing Life Sciences R & D|November 22, 2025
PCIM: Learning pixel attributions via pixel-wise channel isolation mixing in high content imagingDaniel Siegismund, Mario Wieser, Stephan Heyse, et al.
SLAS Technology|January 21, 2022
Benchmarking feature selection methods for compressing image information in high-content screeningDaniel Siegismund, Matthias Fassler, Stephan Heyse, et al.
Materials Science & Engineering. C, Materials for Biological Applications|November 28, 2014
Evaluation of wettability and surface energy of native Nitinol surfaces in relation to hemocompatibilitySvetlana A Shabalovskaya, Daniel Siegismund, Erik Heurich, et al.
Acta Biomaterialia|September 28, 2013
Quantification of the interaction between biomaterial surfaces and bacteria by 3-D modelingDaniel Siegismund, Andreas Undisz, Sebastian Germerodt, et al.
Macromolecular Bioscience|July 6, 2010
Fibrinogen adsorption on biomaterials--a numerical studyDaniel Siegismund, Thomas F Keller, Klaus D Jandt, et al.
Assay and Drug Development Technologies|August 28, 2018
Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown PhenotypesOliver Dürr, Elvis Murina, Daniel Siegismund, et al.
SLAS Discovery : Advancing Life Sciences R & D|February 5, 2026
AI-Based Analysis of Label-Free Live Cell Imaging of T-Cell Mediated Tumor Killing Assay Enables Competitive and Robust Hit CallingJosefa Chuh, Daniel Siegismund, John Moffat, et al.
Drug Research|January 18, 2018
Developing Deep Learning Applications for Life Science and Pharma IndustryDaniel Siegismund, Vasily Tolkachev, Stephan Heyse, et al.
SLAS Discovery : Advancing Life Sciences R & D|March 22, 2026
Response to Letter Regarding "AI‑based analysis of label‑free live‑cell imaging of T‑cell-mediated tumor killing"Josefa Chuh, Daniel Siegismund, John Moffat, et al.
SLAS Discovery : Advancing Life Sciences R & D|May 21, 2020
Deep Learning-Based HCS Image Analysis for the EnterpriseStephan Steigele, Daniel Siegismund, Matthias Fassler, et al.
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