Phd Position in Machine Learning for Liver Cancer

vor 13 Stunden

Lausanne, Schweiz Centre hospitalier universitaire vaudois Vollzeit

**Contexte**:
The Lausanne University Hospital (CHUV) is one of five Swiss university hospitals. Through its collaboration with the Faculty of Biology and Medicine of the University of Lausanne and the EPFL, CHUV plays a leading role in the areas of medical care, medical research and training.

The Radiology Department has a strong research focus, with several groups dedicated to advancing magnetic resonance imaging (MRI) acquisition, improving image processing and machine learning for radiology, as well as radiologists that are very active in clinical research. The department is also part of the Center for Biomedical Imaging, a joint undertaking of the EPFL, University of Geneva, University of Lausanne, and Geneva University Hospital, and enjoys regular collaborations with these institutions.

The _Improving hepatocellular carcinoma screening in Switzerland : new strategies_ project, funded by the Swiss National Science Foundation, aims at validating new, short MRI sequences for liver cancer detection. The project, anchored at the Radiology Department, will be led in close collaboration between experts in abdominal and oncological radiology, hepatology, oncology, pathology, health economics, and machine learning for biomedical data at the Lausanne University Hospital and University of Lausanne.

**Mission**:
**PROJECT DESCRIPTION**

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide and the fifth in Switzerland. The stage of disease at time of diagnosis determines prognosis and treatment effectiveness. Currently, all patients at risk of HCC perform a semi-annual screening with ultrasound. However, this technique is known for limited sensitivity for HCC detection. The novel idea of abbreviated MRI (AMRI) consists in the selection of sequences designed to answer the targeted question of HCC detection, thus reducing time and cost while maintaining high diagnostic accuracy. Out of the several options for AMRI protocols, Dynamic AMRI (Dyn-AMRI) seems to be the most accurate protocol for screening. However, it has not been validated prospectively in terms of sensitivity, specificity, and cost-effectiveness compared to the standard of care (ultrasound). Non-contrast AMRI (NC-AMRI) is also a potential alternative. In this context, machine learning approaches, and in particular deep learning techniques from computer vision, offer an opportunity to improve lesion detection sensitivity and/or reduce image interpretation time, both important aspects of an effective screening strategy.

The goal of this three-year doctoral position, in collaboration with our other project partners, will be to develop new machine learning algorithms for HCC lesion detection from the different AMRI data, including dynamic T1 weighted imaging (w) (volumetric time series), T2w, and diffusion weighted imaging (DWI). Using available, in-house dense ground truth segmentations, we will use a detection-by-segmentation approach initially based on a classical multichannel 3D U-Net architecture. We will modify the architecture to leverage the temporal dimension of the T1w data, and combine these improvements with vision transformer-style architectures. Finally, we will explore the added value in terms of lesion detection and characterization (multi-output network). We will benchmark our work with respect to state-of-the-art approaches in open data challenges, such as the nn-Unet and UNETR. The work is expected to be translational and will contribute evidence towards improving clinical guidelines for HCC screening, and other project partners will combine our work with a cost-effectiveness analysis using health economics models.

**Profil**:

- MSc in computer science, electrical engineering, biomedical engineering, statistical physics, applied mathematics, statistics, or related field
- Good training in linear algebra, calculus, statistics
- Good knowledge of Python 3.8+, including relevant machine learning libraries such as scikit-learn 1.0+, Pytorch 1.8+,Tensorflow 2.4+.
- English proficiency necessary, French knowledge an asset
- Demonstrated previous experience in machine learning and deep learning required
- Experience in medical imaging, image processing, computer vision are all an advantage.

You will be part of a team of engineers, radiologists, clinicians, and economists with diverse backgrounds and expertises, which requires a strong communication ability and professionalism. Excellent inter-personal skills are as important as technical skills. This project will also require creative spirit and the ability to work autonomously, although you will collaborate very closely with project partners.

**Nous offrons**:
To become an employee of the world-famous University Hospital Center from the Canton of Vaud is an assurance of:

- First-rate social benefits such as a Paternity Leave of 20 days and a Maternity Leave of 4 months (there is also the possibility to obtain a complementary breastfeeding leave

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