Paper on head and neck tumor segmentation accepted at MIDL 2020

Our paper on Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT has been accepted for presentation at the international conference on Medical Imaging with Deep Learning (MIDL). The conference will be fully virtual 6-8 July. In this paper, we developed a baseline for our MICCAI challenge (HECKTOR, more information to come).

head and neck tumor segmentation in PET-CT image

Paper accepted at EMBC 2020

Our paper entitled “Training a Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading”, by Marek Wodzinski, Tommaso Banzato, Manfredo Atzori, Vincent Andrearczyk, Yashin Dicente Cid and Henning Müller, has been accepted for presentation at EMBC 2020, the International Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the Conference of the Canadian Medical and Biological Engineering Society.

Paper published in European Radiology Experimental

The paper “Integrating radiomics into holomics for personalised oncology: from algorithms to bedside” by Roberto Gatta, Adrien Depeursinge, Osman Ratib, Olivier Michielin, and Antoine Leimgruber has been accepted for publication in European Radiology Experimental.

This work is the result of a collaboration between CHUV, HES-SO, and the Riviera-Chablais Hospital.

The full text is now available online at this link.

New paper accepted in Scientific Data

The paper entitled “Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics“, by Matteo Cognolato, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Katia Giacomino, Anne-Gabrielle Mittaz Hager, Andrea Gigli, Diego Faccio, Cesare Tiengo, Franco Bassetto, Barbara Caputo, Peter Brugger, Manfredo Atzori and Henning Müller has been accepted and published in Scientific Data, a journal from the publishers of Nature.

Find MedGIFT on Medium

Don’t miss our new Medium publication page. You can find there technical tutorials, stories and insights on our research focus, and updates from conferences.

Some of our latest posts are:

How docker changed my life: deploying machine learning has never been this easy!

A simple formula for complex research: how to explain the role of AI to non- geeks

The prosthesis that everybody wants: working with amputees to design inexpensive prosthetic hands that work well

How to ‘crack the code’ of the developing brain?

Marie Curie Fellowship awarded to Cristina Simon-Martinez!

Cristina Simon-Martinez has been awarded a prestigious Individual Fellowship of the Marie Curie Actions, funded by the European Union, for the project Optimizing Vision reHABilition with virtual-reality games in pediatric amblyopia (V-HAB). The grant lasts for 2 years and offers exciting new learning opportunities, also contributing to the training, networking and research costs of the fellow. With this grant, Cristina will study the impact of vision deficits on cognition and motor control in children with pediatric amblyopia and will use virtual reality as a means to reduce those deficits.

Presentation of Vincent Andrearczyk at MMM2020

Vincent Andrearczyk presented our work on exploiting images from public biomedical literature for machine learning at the 26th international conference on MultiMedia Modelling (MMM2020) in Daejeon, Korea.

Full details of the paper: “Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction” by Henning Müller, Vincent Andrearczyk, Oscar Jimenez del Toro, Anjani Dhrangadhariya, Roger Schaer and Manfredo Atzori.

Invited talk of Valentin Oreiller at the 2019 Workshop on Machine Learning in Radiology

Valentin Oreiller gave an invited talk on “Learning with less data: Parametric and Locally Rotation Invariant CNNs” at the Workshop on Machine Learning in Radiology 2019 in CHUV (Centre Hospitalier Universaire Vaudois), Lausanne.

This workshop, co-organized by CHUV Department of Radiology and Siemens Healthineers Advanced Clinical Imaging Technology, gathered national and international researchers for discussions and talks on the use of machine learning with radiology data, and how these techniques can benefit clinical practice.