Research Projects

Since 2024: Survival Prediction Using Pancreatic Tissue Microscopy Images

Image analysis, deep learning, histopathology, oncology
PANCALYZE project, conducted in collaboration with the University Hospital of Cologne.
Development of deep learning algorithms for the prediction of patient survival using tissue microarray images.

Since 2023: Automated Segmentation and Characterization of Renal Tissue Microscopy Images

Image analysis, deep learning, histopathology, nephrology
FOrMe project, in collaboration with the University Hospital of Cologne.
Development of deep learning algorithms for the analysis of renal tissue to characterize the nephrotic syndrome. Contributions:

• New neural network architectures for glomeruli segmentation, available on GitHub.

2020 - 2023: Automated Segmentation and Characterization of Lesions in PET/CT Images of Metastatic Breast Cancer Patients

Image analysis, deep learning, radiology, oncology
Doctoral Thesis (CIFRE program) conducted with Keosys Medical Imaging, the ICO cancer center, and the LS2N Laboratory as part of the EPICURE project.
Development of deep learning algorithms for the segmentation of metastases on longitudinal PET/CT images to assess the response to treatments of patients with a metastatic breast cancer.
Contributions:

• UNet model for PET/CT lesion segmentation.
• New method for the segmentation of metastases on longitudinal whole-body PET/CT images.
• Two new biomarkers for evaluating patient response to treatments.

2019: Semi-Automatic Segmentation of Lesions in PET/CT Images of Metastatic Breast Cancer Patients

Image analysis, deep learning, radiology, oncology
Internship at Keosys Medical Imaging, comparing semi-automatic segmentation algorithms from the literature with a deep learning-based method.
Contributions:

• Integrated segmentation algorithms into Keosys’s medical image visualization and annotation software.