Aortic shunting operations need to be planned accurately. To do this, surgeons need to know exact sizes of aorta. This project creates a tool for medical imaging software, which will automate measuring aorta using computer vision and machine learning methods. Results of this project will be later used for other applications in medical image analysis.

Semantic indexing for edge devices.

Building a small, accurate index for large scale text and images datasets to be used on edge devices. Direct document recommendation, distributed indexing strategy, duplicate detection, content-based image retrieval.

Domain Adaptation.

Investigation the effect of domain gaps on the performance of machine learning models performances for different applications. Our first step was to measure the change in datasets and propose a Domain Adaptation technique for fixing this problem in images, which we extended to work on video datasets and text. We also proposed a new method for domain généralisation based on adversarial reconstruction loss. We are currently investigating the effect of deploying the same model for medical imaging on different MRI scans (MRI-Sequences).

Neural Machine Translation.

Our goal is to find new effective ways to leverage such additional sources as monolingual data or parallel corpora for related languages. Together with our students, we made the review of the current approaches to utilize monolingual data in low-resource machine translation.