My research topics mainly focus on the automatic segmentation of multidimensional medical data. These topics are part of the team Myriad.
An important part of my work has been dedicated to Mean-shift, which is an exciting approach when no assumptions are made about the data. Mean-shift can be derived in a knowledge discovery framework. Extending this framework to spatiotemporal data, scale, and space selections, with the integration of a few prior, consisted of my main research. Such methods are motivated by medical challenges ( Multiple sclerosis, stroke, cardiac segmentation…. ).
Now, I am focusing on approaches based on deep-learning for segmentation, filtering, and localization/detection tasks that can provide better and more robust results than many conventional approaches. The main challenges are the use of semi- and weakly-supervised methods, dealing when experts disagree, and trying to train in a playful manner (selection of needed data, continuous learning, …). The applications of such segmentations are: pathologies understanding and quantification, longitudinal analysis (disease evolution, clinical care) and more and more simulations (CFD, failure load… digital twin).
For image processing application development, my background are on ITK and OpenCV, with QTCreator and CMake for C++, under Windows and Linux. I use more and more Python with conda and really appreciate the jupyter lab environment (debug, contextual help !).
For deep learning, I mostly use Python with Keras/Tensorflow (using conda), or MONAI . For Yolo, I switch from AlexeyAB Darknet lib to Ultralytics one.