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  5. Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

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Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

(FAU Funds)

Overall project:
Project leader: Andreas Maier, Sulaiman Vesal
Project members: Dalia Rodríguez Salas
Start date: March 1, 2017
End date: October 31, 2019
Acronym:
Funding source:
URL:

Abstract

Breast cancer is the leading cause of cancer related deaths in women, the second most common cancer worldwide. The development and progression of breast cancer is a dynamic biological and evolutionary process. It involves a composite organ system, with transcriptome shaped by gene aberrations, epigenetic changes, the cellular biological context, and environmental influences. Breast cancer growth and response to treatment has a number of characteristics that are specific to the individual patient, for example the response of the immune system and the interaction with the neighboring tissue. The overall complexity of breast cancer is the main cause for the current, unsatisfying understanding of its development and the patient’s therapy response. Although recent precision medicine approaches, including genomic characterization and immunotherapies, have shown clear improvements with regard to prognosis, the right treatment of this disease remains a serious challenge. The vision of the BIG-THERA team is to improve individualized breast cancer diagnostics and therapy, with the ultimate goal of extending the life expectancy of breast cancer sufferers. Our primary contribution in this regard is developing a multi-modal, multi-scale framework for the early diagnosis of the molecular sub-types of breast cancer, in a manner that supplements the clinical diagnostic workflow and enables the early identification of patients compatible with specific immunotherapeutic solutions.

Publications

  • Vesal S., Ravikumar N., Davari A., Ellmann S., Maier A.:
    Classification of Breast Cancer Histology Images Using Transfer Learning
    International Conference Image Analysis and Recognition (, June 12, 2018)
    In: ICIAR 2018: Image Analysis and Recognition 2018
    DOI: 10.1007/978-3-319-93000-8_92
    BibTeX: Download
  • Kaiser N., Fieselmann A., Vesal S., Ravikumar N., Ritschl L., Kappler S., Maier A.:
    Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability
    Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (San Diego, CA, February 20, 2019 - February 21, 2019)
    In: MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, BELLINGHAM: 2019
    DOI: 10.1117/12.2513420
    BibTeX: Download
  • Vesal S., Diaz-Pinto A., Ravikumar N., Ellmann S., Davari A., Maier A.:
    Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
    IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (Atlanta, Georgia, USA)
    In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2017
    URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Vesal17-SAF.pdf
    BibTeX: Download
  • Ellmann S., Wenkel E., Dietzel M., Bielowski C., Vesal S., Maier A., Hammon M., Janka RM., Fasching P., Beckmann M., Schulz-Wendtland R., Uder M., Bäuerle T.:
    Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses
    In: PLoS ONE 1-15 (2020), p. 1-15
    ISSN: 1932-6203
    DOI: 10.1371/journal.pone.0228446
    BibTeX: Download
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