News

We will be presenting our work at two conferences: the Swiss Academy of Childhood Disability (SACD), 20th January 2022 and the European Academy of Childhood Disability (EACD), which will take place 18-21 May 2022 in Barcelona.

At SACD, pour paper “Serious games embedded in virtual reality as a visual rehabilitation tool for individuals with pediatric amblyopia: A protocol for a crossover randomized controlled trial“, by C. Simon-Martinez , B. Backus , B. Dornbos , M.-P. Antoniou , M. Kropp, G. Thumann, W. Bouthour, H. Steffen and P. J. Matusz, received the 2nd prize on the category of study protocols.

At EACD, we will present our work on “The use of DeepLabCut to detect and quantify mirror movements in children with unilateral cerebral palsy“, by B. Berclaz, H. Haberfehlner, K. Klingels, H. Feys, P. Meyns, H. Müller and C. Simon-Martinez

At EACD, Cristina Simon-Martinez is also co-author on 2 other abstracts, one with the KU Leuven and one with the University Hospital in Bern, that were accepted as oral presentations! Meet our team there to discuss all this amazing work.

Our work on “Improving Robotic Hand Prostheses Control with Eye Tracking and Computer Vision: a Multimodal Approach based on the Visuomotor Behavior of Grasping“, by M. Cognolato, M. Atzori, R. Gassert and H. Müller, was published in Frontiers in Artificial Intelligence, section Machine Learning and Artificial Intelligence.

In this paper, we investigate a multimodal approach that exploits the use of eye-hand coordination to improve the control of myoelectric hand prostheses.

Full paper available open-access online: https://www.frontiersin.org/articles/10.3389/frai.2021.744476/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Artificial_Intelligence&id=744476

Our article presenting results and in depth analyses of the first HEad and neCK TumOR Segmentation (HECKTOR 2020) challenge was published in Medical Image Analysis (Elsevier).

Link to the article: https://doi.org/10.1016/j.media.2021.102336

Mara Graziani successfully defended her PhD thesis on the interpretability of deep learning for medical image analysis. This excellent work was supervised by Prof. Stephane Marchand-Maillet (UNIGE) and, Prof. Henning Müller (HES-SO, UNIGE). Prof. Mauricio Reyes (UniBE) was part of the external committee.

Our abstract presenting results of the Image Biomarker Standardisation Initiative (IBSI) on reproducible convolutional radiomics has been accepted for presentation at the European Congress of Radiology (ECR 2022).

It will be presented onsite in Vienna during ECR 2022 in July.

Prof. Martin Vallières received the Cum Laude award for his education exhibit at Radiological Society of North America (RSNA) 2021, presenting Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI): Benchmarks And Guidelines

FishLab, led by the COREALIS and our institute of information systems at HES-SO, will analyze near real-time fish flows and migration using machine learning algorithms on videos. Preliminary experiments with three stations located in the Rhône and the Aare rivers already enabled the improvement of the embedded system to detect and count fish.

More information (in french or in german): here

Our paper on “Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study“, Giulio Marano et al. has been published in Sensors as part of a Special Issue on Biomedical Sensors for Functional Mapping.

In this study, we question the benefit of domain adaptation in transfer learning techniques (using pre-trained models obtained from prior subjects) applied to machine learning algorithms for automatic myoelectric hand prostheses control.

For more information, check the paper now available online: https://www.mdpi.com/1424-8220/21/22/7500/pdf

On Sept. 27, we hosted the HECKTOR2021 challenge as a satellite event of MICCAI 2021. We presented the excellent results obtained by the various international teams on the tasks of tumor segmentation and outcome prediction in head and neck cancer.

We had great participation in the event with lively interaction with the participants, and a very interesting keynote by Prof. Clifton Fuller.

The Leaderboard of the challemge is available online: https://www.aicrowd.com/challenges/miccai-2021-hecktor/leaderboards

Stay tuned for the LNCS proceedings reporting the methods and results.

Thank you to all the participants, sponsors and organizers! We look forward to next year’s challenge.

The 7^th edition of the Valais/Wallis AI Workshop will take place on Nov. 15 2021 at the IDIAP research institute with a live youtube streaming (hybrid event).

Organized by IDIAP and HES-SO Valais, it will feature various presentations and discussions on “Energy in all its artificial states”, including a keynote presentation by Pr Guglielmina Mutani.

Full program and registration are now available online: https://www.idiap.ch/workshop/valais-wallis-ai-workshop/program

The proceedings of the Conference and Labs of the Evaluation Forum (CLEF) 2021 conference are now available online: https://link.springer.com/book/10.1007%2F978-3-030-85251-1

It includes various interesting papers on information retrieval as well as overview reports of the different labs including ImageCLEF and LifeCLEF.

We will be presenting our two papers on Head and Neck tumor segmentation and prognostic prediction, and one paper on colorectal cancer detection in whole slide images at MICCAI workshops Sept. 27th and Oct. 1st. Together with our paper at the main conference and the organization of the HECKTOR challenge, we are happy to have a total of five great contributions to MICCAI this year.

• Our paper on Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer will be presented by Vincent Andrearczyk at PRIME 2021.

• Our other paper on Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT will be presented by Pierre Fontaine at ML-CDS 2021.

• Our third workshop paper on Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations will be presented by Niccolò Marini at COMPAY 2021

Our review paper on harmonization methods in radiomics, in collaboration with the School for Oncology, Maastricht University, has been published in the Journal of Personalized Medicine.

The paper is now available online: “Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods“, Shruti Atul Mali et al.

Radiomics aims to extract quantitative features from medical images to improve decision support. In this work, we discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. The different harmonization solutions are divided into two main categories: image domain (including image acquisition, post-processing of raw sensor-level image data, data augmentation, and style transfer) and feature domain (including statistical normalization, intensity harmonization, ComBat and deep learning harmonization).

Our paper on “Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images” by Niccolò Marini et al. has been published in Frontiers in Computer Science, section Digital Public Health.

This paper describes our python library called Multi_Scale_Tools. It includes four components:

• a pre-processing component with two methods to split the WSIs in patches from arbitrary magnification levels,
• a CNN-based scale regressor to predict the magnification in a continuous range between 5-40x,
• two multi-scale CNN architectures to make predictions at patch-level and
• a multi-scale CNN architecture called HookNet to segment WSIs.
  

The code is available on GitHub: https://github.com/sara-nl/multi-scale-tools

The emulation of the HECKTOR challenge at MICCAI2021 is on with many active participants! The test data has just been released and the submission website will be open from Sep. 01 to Sep. 10. We are very much looking forward to your participation !

More info here: https://www.aicrowd.com/challenges/miccai-2021-hecktor

We are very grateful to the HECKTOR 2021 sponsors:

Task 1 (segmentation) is sponsored by Siemens Healthineers Switzerland with a prize of 500 €.
Task 2 (prediction of PFS) is sponsored by Aquilab with a prize of 500 €.
Task 3 (prediction of PFS using ground-truth delineation of tumors) is sponsored by Biomemtech with a prize of 500 €.

Our paper “Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability” by Mara Graziani, Iam Palatnik De Sousa et. al will be presented at MICCAI 2021, Sept. 27 to Oct 1st, Strasbourg, France.

In this work, we improve the application of LIME to histopathology images by leveraging nuclei annotations. The obtained visualizations reveal the sharp, neat and high attention of the deep classifier to the neoplastic nuclei in the dataset, an observation in line with clinical decision making.
Compared to standard LIME, our explanations show improved understandability for domain-experts, report higher stability and pass the sanity checks of consistency to data or initialization changes and sensitivity to network parameters.

Congratulations Dr. Otálora for this well-deserved achievement!

Sebastian successfully defended his PhD thesis entitled “Deep Learning for Histopathology Image Analysis from Heterogeneous & Multimodal Data Sources” supervised by Prof. Stephane Marchand-Maillet and, Prof. Henning Müller.

Our work on “The Discriminative Power and Stability of Radiomics Features With Computed Tomography Variations“, by Oscar Jimenez-del-Toro et al. has been published in Investigative Radiology.

Using a 3D-printed CT phantom based on real patient data, we studied the stability of radiomics features against CT parameter variations and their discriminative power for tissue classification.

Our work on “Common limitations of performance metrics in biomedical image analysis” has been accepted for publication in MIDL 2021. Conducted by a large international consortium including the BIAS initiative, the MICCAI Society’s challenge working group and the benchmarking working group of the MONAI framework, this initiative aims at generating best practice recommendations with respect to metrics in medical image analysis.

Full paper available on arxiv: https://arxiv.org/abs/2104.05642

Check out our steerable detectors if you lost your (biological) patterns in noisy backgrounds 🙂 https://ieeexplore.ieee.org/document/9406375

A single-shot template learning will allow finding all occurrences no matter their orientation are and with high recall and specificity ! 

And it is obviously all packaged in an open-source ImageJ plugin!
http://bigwww.epfl.ch/algorithms/steer_n_detect

Plus open source code on GitHub:
https://github.com/Biomedical-Imaging-Group/Steer-n-Detect

Joint work with Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki, Benjamin Beck and Michael Unser.