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Research Department Photonic Data Science

Scientific Profile

The research department investigates the entire data life cycle of photonic data, which extends from data generation to evaluation and archiving. The data life cycle is considered in a holistic approach and methods and algorithms for experiment planning, sample size planning, data pretreatment and data standardization are investigated. These methods are combined with chemometric methods, model transfer techniques and artificial intelligence methods in a data pipeline. This holistic approach makes it possible to use data from various photonic methods for analysis, diagnostics and therapy in various fields of application, e.g. medicine, the life and environmental sciences and pharmacy.

Further focal points of the research department are the data fusion of different heterogeneous data sources, the simulation of different measurement procedures in order to optimize correction procedures, methods for the interpretation of analysis models and the construction of data infrastructures for different photonic measurement data, which guarantee the FAIR principles.

Research Topics

  • Machine learning for photonic image data, Publications [1-4]
  • Chemometrics for spectral data, Publications [5-8]
  • Correlation of different measurement methods and data fusion, Publications [9-11]

The work of the working group provides the basis for the application of new photonic methods for bio-medical questions. The evaluation methods are not only developed, researched and improved, but these evaluation methods are also tested in the application context, such as clinical studies.  

Addressed application fields

  • Bio-medical diagnostics using spectral measurement methods
  • Bio-medical diagnostics using imaging measurement techniques
  • Extraction of higher information from photonic measurement data
  • Guarantee of FAIR principles for photonic data

Publications

  1. S. Guo; T. Bocklitz & J. Popp Optimization of Raman-Spectrum Baseline Correction in Biological Application Analyst, The Royal Society of Chemistry, 2016, 141, 2396-2404
  2. S. Guo; T. Bocklitz; U. Neugebauer & J. Popp Common Mistakes in Cross-Validating Classification Models Analytical Methods, 2017, 9, 4410-4417
  3. N. Ali; S. Girnus; P. Roesch; J. Popp & T. W. Bocklitz Sample size planning for multivariate data: a Raman spectroscopy based example Anal. Chem., Analytical Chemistry, American Chemical Society, 2018, 90, 12485-12492
  4. S. Guo; A. Kohler; B. Zimmermann; R. Heinke; S. Stöckel; P. Rösch; J. Popp & T. W. Bocklitz EMSC Based Model Transfer for Raman Spectroscopy in Biological Applications Anal. Chem., Analytical Chemistry, American Chemical Society, 2018, 90, 9787-9795
  5. T. Bocklitz; F. S. Salah; N. Vogler; S. Heuke; O. Chernavskaia; C. Schmidt; M. Waldner; F. R. Greten; R. Bräuer; M. Schmitt; A. Stallmach; I. Petersen & Jü. Popp Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool BMC Cancer, 2016, 16, 1-11
  6. O. Chernavskaia; T. Bocklitz; T. Meyer; N. Vogler; D. Akimov; S. Heuke; S. Guo; R. Heintzmann & J. Popp Correction of mosaicking artefacts in multimodal images caused by uneven illumination J. Chemom., 2017, 31, e2908
  7. E. Rodner; T. Bocklitz; F. von Eggeling; G. Ernst; O. Chernavskaia; J. Popp; J. Denzler & O. Guntinas-Lichius Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: A pilot study Head & Neck, 2019, 41, 116-121
  8. P. Pradhan; T. Meyer; M. Vieth; A. Stallmach; M. Waldner; M. Schmitt; J. Popp & T. Bocklitz Semantic segmentation of Non-Linear Multimodal images for disease grading of Inflammatory Bowel Disease -- A SegNet-based application Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, SciTePress, 2019, 396-405
  9. O. Ryabchykov; J. Popp & T. Bocklitz Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples Frontiers in Chemistry, 2018, 6, 257
  10. T. Bocklitz; T. Meyer; M. Schmitt; I. Rimke; F. Hoffmann; F. von Eggeling; G. Ernst; O. Guntinas-Lichius & J. Popp Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications APL Photonics, 2018, 3, 092404
  11. R. Geitner; R. Fritzsch; J. Popp & T. W. Bocklitz corr2D -- Implementation of Two-Dimensional Correlation Analysis in R J. Stat. Softw., 2019, 90, 1-10
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