Fig 3

Photonic Data Science

Research group Prof. Dr. Thomas Bocklitz
Fig 3
Graphic: IPHT

Head of the group

Thomas Bocklitz, Univ.-Prof. Dr
Head
Professorship of Photonic Data Science
Prof. Dr. Thomas Bocklitz
Image: Prof. Dr. Thomas Bocklitz
JenTower
Leutragraben 1
07743 Jena Google Maps site planExternal link

Scientific Profile

We explore the entire data life cycle of photonic data from generation to the data analysis and to data archiving. Following a holistic approach, we investigate procedures for experiment and sample size planning as well as data pretreatment and combine these procedures with chemometric procedures, model transfer methods and artificial intelligence methods in a data pipeline. In this way, data from various photonic processes can be used for analysis, diagnostics and therapy in medicine, life science, environmental sciences and pharmacy. The data pipeline are implemented in software components and are tested directly in the applicative environment, e.g. in clinical studies. Further focal points are 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 ensure the FAIR principles.

Research Topics

  • Machine learning for photonic image data
    Fig 1

    Fig 1

    Graphic: IPHT
  • Chemometrics / machine learning for spectral data
  • Correlation of different measurement methods and data fusion

Areas of application

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

Staff

  1. Adhikari, Mou PhD Student Professorship of Photonic Data Science

    JenTower, Room 15S04
    Leutragraben 1
    07743 Jena

  2. Amiri, Mahmoud Professorship of Photonic Data Science

    JenTower
    Leutragraben 1
    07743 Jena

  3. Azam, Kazi Sultana Farhana PhD student Professorship of Photonic Data Science

    JenTower, Room 15S04
    Leutragraben 1
    07743 Jena

  4. Contreras Duarte, Jhonatan PostDoc Professorship of Photonic Data Science

    JenTower, Room 15S02
    Leutragraben 1
    07743 Jena

  5. Corbetta, Elena PhD student Professorship of Photonic Data Science

    JenTower, Room 15S05
    Leutragraben 1
    07743 Jena

  6. Darzi, Fatemehzahra PhD student Professorship of Photonic Data Science

    JenTower, Room 15S03
    Leutragraben 1
    07743 Jena

  7. Dehbozorgi, Pegah PhD Student Professorship of Photonic Data Science

    JenTower, Room 15S03
    Leutragraben 1
    07743 Jena

  8. Kamran, Jawad Professorship of Photonic Data Science

    JenTower, Room 15S07
    Leutragraben 1
    07743 Jena

  9. Mokari, Azadeh PhD Student Professorship of Photonic Data Science

    JenTower, Room 15S01
    Leutragraben 1
    07743 Jena

  10. Morgunov, Volodymyr, Dr PostDoc Professorship of Photonic Data Science

    JenTower, Room 15S07
    Leutragraben 1
    07743 Jena

    Dr. Volodymyr Morgunov
    Image: Volodymyr Morgunov
  11. Mostafapourghasrodashti, Sara PhD student Professorship of Photonic Data Science
  12. Ryabchykov, Oleg, Dr PostDoc Professorship of Photonic Data Science

    JenTower, Room 15S02
    Leutragraben 1
    07743 Jena

  13. Stefaniuk, Nazar PostDoc Professorship of Photonic Data Science

    JenTower
    Leutragraben 1
    07743 Jena

  14. Thamm, Sophie, Dr Coordinator LPI Professorship of Photonic Data Science

    JenTower, Room 15S08
    Leutragraben 1
    07743 Jena

  15. Vulchi, Ravi Teja PhD Student Professorship of Photonic Data Science

    JenTower
    Leutragraben 1
    07743 Jena

  16. Yogita, Yogita PhD student Professorship of Photonic Data Science

    JenTower, Room 15S01
    Leutragraben 1
    07743 Jena

60 Publikationen filtern

Die Publikationen filtern

Highlighted authors are members of the University of Jena.

  1. A comparative study of robustness to noise and interpretability in U-Net-based denoising of Raman spectra

    Year of publicationStatusReview pendingPublished in:Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy A. Mokari, S. Eiserloh, O. Ryabchykov, U. Neugebauer, T. Bocklitz
    University Bibliography Jena:
    fsu_mods_00026011External link

  2. Bridging Spectral Gaps: Cross-Device Model Generalization in Blood-Based Infrared Spectroscopy

    Year of publicationPublished in:Analytical Chemistry F. Nemeth, N. Leopold-Kerschbaumer, D. Debreceni, F. Fleischmann, K. Borbely, D. Mazurencu-Marinescu-Pele, T. Bocklitz, M. Žigman, K. Kepesidis
    This paper presents a solution to the challenge of cross-device model generalization in blood-based infrared spectroscopy. As infrared spectroscopy becomes increasingly popular for analyzing human blood, ensuring that machine learning models trained on one device can be effectively transferred to others is essential. However, variations in device characteristics often reduce model performance when applied across different devices. To address this issue, we propose a straightforward domain adaptation method based on data augmentation incorporating device-specific differences. By expanding the training data to include a broader range of nuances, our approach enhances the model’s ability to adapt to the unique characteristics of various devices. We validate the effectiveness of our method through experimental testing on two Fourier-Transform Infrared (FTIR) spectroscopy devices from different research laboratories, demonstrating improved prediction accuracy and reliability.
    University Bibliography Jena:
    fsu_mods_00024831External link

  3. Enhancing prediction stability and performance in LIBS analysis using custom CNN architectures

    Year of publicationPublished in:Talanta: the international journal of pure and applied analytical chemistry P. Dehbozorgi, L. Duponchel, V. Motto-Ros, T. Bocklitz
    LIBS-based analysis has experienced an ever-increasing interest in recent years as a well-suited technique for chemical analysis tasks relying on elemental fingerprinting. This method stands out for its ability to offer rapid, simultaneous multi-element analysis with the advantage of portability. In the context of this research, our aim is to bridge the gap between the analysis of simulated and real data to better account for variations in plasma temperature and electron density, which are typically not considered in LIBS analysis. To achieve this, we employ two distinct methodologies, PLS and CNNs, to construct predictive models for predicting the concentration of the 24 elements within each LIBS spectrum. The initial phase of our investigation concentrates on the training and testing of these models using simulated LIBS data, with results evaluated through RMSEP values. The IQR and median RMSEP values for all the elements demonstrate that CNNs consistently achieved values below 0.01, while PLS results ranged from 0.01 to 0.05, highlighting the superior stability and predictive accuracy of CNNs model. In the next phase, we applied the pre-trained models to analyze the real LIBS spectra, consistently identifying Aluminum (Al), Iron (Fe), and Silicon (Si) as having the highest predicted concentrations. The overall predicted values were approximately 0.5 for Al, 0.6 for Si, and 0.04 for Fe. In the third phase, deliberate adjustments are made to the training parameters and architecture of the proposed CNNs model to force the network to emphasize specific elements, prioritizing them over other components present in each real LIBS spectrum. The generation of the three modified versions of the initially proposed CNNs allows us to explore the impact of regularization, sample weighting, and a customized loss function on prediction outcomes. Some elements emerge during the prediction phase, with Calcium (Ca), Magnesium (Mg), Zinc (Zn), Titanium (Ti), and Gallium (Ga) exhibiting more pronounced patterns.
    University Bibliography Jena:
    fsu_mods_00018242External link

  4. Explainable artificial intelligence for spectroscopy data: a review

    Year of publicationPublished in:Pflügers Archiv : European journal of physiology J. Contreras, T. Bocklitz
    Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation. Following the PRISMA guideline 2020, we conducted a systematic search across major journal databases, resulting in 259 initial search results. After removing duplicates and applying inclusion and exclusion criteria, 21 scientific studies were included in this review. Notably, most of the studies focused on using XAI methods for spectral data analysis, emphasizing identifying significant spectral bands rather than specific intensity peaks. Among the most utilized AI techniques were SHapley Additive exPlanations (SHAP), masking methods inspired by Local Interpretable Model-agnostic Explanations (LIME), and Class Activation Mapping (CAM). These methods were favored due to their model-agnostic nature and ease of use, enabling interpretable explanations without modifying the original models. Future research should propose new methods and explore the adaptation of other XAI employed in other domains to better suit the unique characteristics of spectroscopic data.
    University Bibliography Jena:
    fsu_mods_00016079External link

  5. Machine Learning-Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

    Year of publicationPublished in:Advanced Intelligent Systems E. Corbetta, T. Bocklitz
    Reliable characterization of image data is fundamental for imaging applications, FAIR data management, and an objective evaluation of image acquisition, processing, and analysis steps in an image-based investigation of biological samples. Image quality assessment (IQA) often relies on human visual perception, which is not objective, or reference ground truth images, which are not often available. This study presents a method for a comprehensive IQA of microscopic images, which solves these issues by employing a set of reference-free metrics that estimate the presence of experimental artifacts. The metrics are jointly validated on a semisynthetic dataset and are tested on experimental images. Finally, the metrics are employed in a machine learning model, demonstrating their effectiveness for automatic artifact classification through multimarker IQA. This work provides a reliable reference-free method for IQA in optical microscopy, which can be integrated into the experimental workflow and tuned to address specific artifact detection tasks.
    University Bibliography Jena:
    fsu_mods_00018351External link

  6. 3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications

    Year of publicationPublished in:Analytical Chemistry R. Luo, S. Guo, J. Hniopek, T. Bocklitz
    Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy. However, most current methods only use 1-dimensional (1D) spectral data classification, instead of considering any neighboring information in space. Despite some successes, this type of methods wastes the 3-dimensional (3D) structure of Raman hyperspectral scans. Therefore, to investigate the feasibility of preserving the spatial information on Raman spectroscopy for data analysis, spatially aware deep learning algorithms were applied into a colorectal tissue data set with 3D Raman hyperspectral scans. This data set contains Raman spectra from normal, hyperplasia, adenoma, carcinoma tissues as well as artifacts. First, a modified version of 3D U-Net was utilized for segmentation; second, another convolutional neural network (CNN) using 3D Raman patches was utilized for pixel-wise classification. Both methods were compared with the conventional 1D CNN method, which worked as baseline. Based on the results of both epithelial tissue detection and colorectal cancer detection, it is shown that using spatially neighboring information on 3D Raman scans can increase the performance of deep learning models, although it might also increase the complexity of network training. Apart from the colorectal tissue data set, experiments were also conducted on a cholangiocarcinoma data set for generalizability verification. The findings in this study can also be potentially applied into future tasks regarding spectroscopic data analysis, especially for improving model performance in a spatially aware way.
    University Bibliography Jena:
    fsu_mods_00024120External link

  7. Correction to: SERS-Driven Ceftriaxone Detection in Blood Plasma: A Protein Precipitation Approach (Chemosensors, (2024), 12, 10, (213), 10.3390/chemosensors12100213)

    Year of publicationPublished in:Chemosensors A. Dwivedi, O. Ryabchykov, C. Liu, E. Farnesi, M. Schmidt, T. Bocklitz, J. Popp, D. Cialla-May
    The blood-based samples for the original publication [1] were purchased from the Institut für Klinische Transfusionsmedizin Jena gGmbH (Jena, Germany). Thus, no ethics approval and informed consent are needed. However, the Institutional Review Board Statement of the paper should not be “Not applicable”, and it should be changed to a specific reason why the ethics approval can be waived. A correction has been made to Institutional Review Board Statement: Institutional Review Board Statement: The blood samples were purchased from the Institut für Klinische Transfusionsmedizin Jena gGmbH (Jena, Germany). All methods were performed in accordance with the relevant guidelines and regulations of the clinics of Universitätsklinikum Jena, thus the blood samples are exempt from the need for ethical approval. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
    University Bibliography Jena:
    fsu_mods_00020927External link

  8. XAI-2DCOS: Enhancing Interpretability in Spectral Deep Learning Models Through 2D Correlation Spectroscopy

    Year of publicationStatusReview pendingPublished in:Journal of chemometrics : a journal of the Chemometrics Society J. Contreras, T. Bocklitz
    University Bibliography Jena:
    fsu_mods_00026200External link

  10. Transfer-Learning Deep Raman Models Using Semiempirical Quantum Chemistry

    Year of publicationStatusReview pendingPublished in:Journal of chemical information and modeling / publ. by the American Chemical Society J. Kamran, J. Hniopek, T. Bocklitz
    University Bibliography Jena:
    fsu_mods_00025760External link

  12. Physics-informed neural networks for etaloning correction in Raman spectra using inverse modeling

    Year of publicationStatusReview pendingPublished in:Proceedings of SPIE - The International Society for Optical Engineering R. Vulchi, V. Morgunov, T. Bocklitz
    University Bibliography Jena:
    fsu_mods_00024844External link
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