Motivation

Image analysis pipelines based on the segmentation, extraction of features of interest and aggregation of this information for subsequent prediction, allow for apriori and aposteriori analysis in addition to being more “trustworthy”. Indeed as the model is build around objects selected by experts, predictions can be traced back to these elements, thus increasing the trust and interpretability.

We have published the work Feature screening with kernel knockoffs by Benjamin Poignard, Peter Naylor, Héctor Climente-González, Makoto Yamada in AISTATS 2022. We presented the work at the conference in the poster format.

I published my final PhD work entitled Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images in Frontier in Biomedical Signal Processing. The preprint is available on bioarxiv, another version of the paper is available here.

I defended my PhD on the 18^th of December 2019 at 2 pm in amphitheater Lacassagne, slides and manuscript.

The Cancer Genome Atlas, a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. This joint effort between the National Cancer Institute and the National Human Genome Research Institute began in 2006, bringing together researchers from diverse disciplines and multiple institutions.

At the ISBI 2019 held in Venice, Italy, I presented our conference paper Predicting Residual Cancer Burden in a triple negative breast cancer cohort during the poster session. The poster can be found here.

I was a teaching assistant for the segmentation practical sessions for the course Deep Learning For Image Analysis given in the ATHENS Programme by Etienne Decencière, Santiago Velasco-Forero and Thomas Walter. I animated a practical session on tissue segmentation, the notebook and presentation can be found in github and the data can be found here.

I was a teaching assistant for the course of Thomas Walter named Machine Learning for Biomedical Images for the In-depth Tutorial with Practical Session at DS3 Data Science Summer School, August 2018: slides notebook. General talk on neural network methods for segmentation.

The paper ‘Segmentation of Nuclei in Histopathology Images by deep regression of the distance map’ by Peter Naylor, Thomas Walter, Fabien Reyal and Marick Laé has been published in IEEE transactions on medical imaging, 2018.

You can find all the desired information on the main organizers git hub page: Chloe-Agathe Azencott. Practical session of 4 days given to M2 students in Mines-paristech.