Review article published in NeuroImage: Clinical

Our review paper “Automated MS lesion detection and segmentation in clinical workflow: a systematic review” was published in NeuroImage: Clinical.

This work, conducted by Federico Spagnolo and colleagues, is the result of a great collaboration between HES-SO, CHUV and UniBasel. We investigate to what extent automatic tools in multiple sclerosis management fulfill the Quantitative Neuroradiology Initiative (QNI) framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.

New CT Phantom for Radiomics dataset published on TCIA

Our new dataset “Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses (CT-Phantom4Radiomics)” has been published on The Cancer Imaging Archive (TCIA). The aims of this dataset are to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data.

The dataset is available here:

The source code and instructions on how to run the full feature extraction pipeline on the TCIA dataset can be found here:

More details on the QA4IQI project results:

Nataliia Molchanova reveived the Summa Cum Laude Merit Award at ISMRM

Congratulations to Nataliia Molchanova who received the Summa Cum Laude Merit Award at the recent 2023 ISMRM & ISMRT Annual Meeting & Exhibition for the abstract “Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis”.

This recognition indicates an emerging interest in Safe AI supporting medical imaging analysis, highlighting an effort of the organizers to raise awareness about the limitations of deep learning and the necessity of moving towards more trustworthy, explainable, and transparent AI solutions in clinical practice.

Nataliia Molchanova and her work on uncertainty measures for MRI segmentation

Congratulations to Dr. Niccolò Marini !

Niccolò Marini successfully defended his PhD work on “Deep learning methods to reduce the need for annotations for the extraction of knowledge from multimodal heterogeneous medical data”.
His thesis focused on:

  • how to alleviate the need for manual annotations to train deep learning algorithms
  • how to leverage color variability to improve the CNN generalization on unseen data
  • how to learn a more details WSI representation combining multiple magnification levels
  • how to empower the raw-pixel level image representation introducing high-level concepts from textual reports

This work was co-supervised by Prof. Henning Müller and Prof. Manfredo Atzori, Institute of Informatics, University of Applied Sciences Western Switzerland (HESSO), Prof. Stephane Marchand-Maillet, Department of Computer Science, University of Geneva. Prof. Anne Martel, Department of Medical Biophysics, University of Toronto, completed the committee as international expert in the domain.

For more details, check his list of publications.

Interview of Cristina Simon-Martinez on the RehaBot project

Cristina Simon-Martinez was interviewed by Research Works to present the ReHaBot project in a very interesting podcast.

RehaBot is a chatbot between therapists and patients to establish tele-rehabilitation programs and quantify their outcome. This project, led in collaboration with Mario Martinez-Zarzuela, received a warm welcome by research and clinical partners.

Learn more about the project in the podcast available online.

InnoCheque funding obtained for the FishLab project

The project “FishLab – Semi-autonomous system for centralised video-monitoring of fish migration in river systems” has been granted an InnoCheque funding by Innosuisse. This will support our collaboration with FishLab to develop a standardized/automated observatory of fish flows in rivers.

For this purpose, fish ladders are fitted with underwater cameras with infrared backlighting.

The end customers are dam operators, who are required to demonstrate the ecological restoration of their structures by 2030 (Federal Water Protection Act 2011).

At HES-SO, we are studying video processing for the detection of fish, their tracking, count, species identification and size measurement. We are also developing an interface to check and complete the automated results.

Paper on dexterous prosthetic hands at GNB 2023

Our article “A dexterous hand prosthesis based on additive manufacturing”, M. Atzori et al., was accepted at the VIII congress of the Italian Group of Bioengineering, which will be held in June 2023.

The article presents a project that has been ongoing for several years now and that is aimed at the development of low cost and dexterous prosthetic hands to be used in real life conditions.
The tests of the prosthetic hand demonstrate its dexterity and the potential of future systems based on 3D printing and machine learning. More details are available in the paper.

3D printed robotic hand and socket.
Examples of hand grasp tests.

Paper accepted at XAI4CV, Workshop at CVPR 2023

Our work on “Disentangling Neuron Representations with Concept Vectors“, by Laura O’Mahony et al., will be presented at the 2nd Explainable AI for Computer Vision (XAI4CV) Workshop at CVPR, Vancouver June 19, 2023.

Breaking down a deep learning model into interpretable units allows us to better understand how models store representations. However, the occurrence of polysemantic neurons, or neurons that respond to multiple unrelated features, makes interpreting individual neurons challenging. This has led to the search for meaningful directions, known as concept vectors, in activation space instead of looking at individual neurons.

In this work, we propose an interpretability method to disentangle polysemantic neurons into concept vectors consisting of linear combinations of neurons that encapsulate distinct features. We found that monosemantic regions exist in activation space, and features are not axis aligned. Our results suggest that exploring directions, instead of neurons may lead us toward finding coherent fundamental units. We hope this work helps bridge the gap between understanding the fundamental units of models, an important goal of mechanistic interpretability, and concept discovery.

The code is available on GitHub.

Great post by Laura available on Medium.

The proposed method to obtain two concept vectors from a neuron is depicted for a neuron that activates for both apples and sports fields. See full paper for details.

QuantImage v2 cloud platform described in Journal of European Radiology Experimental

QuantImage v2 (QI2) is an open-source web-based platform for no-code clinical radiomics research developed in collaboration with HES-SO and CHUV. It was developed with the aim to empower physicians to play a leading role in clinical radiomics research.

The article describing the platform “QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research“, Daniel Abler et al., has been published in Springer Nature, Journal of European Radiology Experimental.

Physician-centered radiomics research. Physician-centered radiomics envisions medical doctors at the center of the radiomics research, development, and translation cycle

The web page describing QuantImage is available here.

Article on CT phantoms for radiomics published in Medical Physics

Our work on “3D-printed iodine-ink CT phantom for radiomics feature extraction – advantages and challenges“, M. Bach et al., has been published in Medical Physics.

To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. In this work, we analyze in detail the quantitative characteristics of iodine-ink based 3D-printed phantom. Design considerations and the manufacturing process are described. Potential pitfalls for radiomics testing for such phantoms are presented.

CT datasets of the different phantom parts and overall phantom appearance. (a) Transversal CT image of the lung-part, (b) the liver part and (c) the test-patterns. (d) coronal CT image of the phantom. (e) cuboid black phantom positioned on a board in a CT scanner.

LNCS proceedings of HECKTOR 2022 published

The Springer LNCS proceedings of the Third Challenge, HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR) 2022, Held in Conjunction with MICCAI 2022, are now published and available online.

The editors V. Andrearczyk, V. Oreiller, M. Hatt, and A. Depeursinge gathered:

  • 23 papers from participants reporting their methods on the tasks of tumor segmentation and outcome prediciton.
  • An overview paper presenting the data, participation, results etc.

Post-challenge participation is still open on grand-challenge.

Journal article published in Neuroscience

Our recent work on “Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements“, authored by C. Brambilla, M. Atzori, H. Müller, A. d’Avella, and A. Scano, has been published in Neuroscience (Elsevier).

Some of the exciting highlights of the work:

  • Non-negative matrix factorization allows to extract spatial and temporal synergies.
  • Spatial and temporal synergies were extracted from two upper-limb datasets.
  • We showed the existence of EMG spatial and temporal structure with surrogate data.
  • Spatial and temporal synergies may capture different hierarchical levels of motor control.
  • The structure of temporal synergies may be related to intermittent control.
Overview of the study

Henning Müller will present the ExaMode project at the AI-Cafe

The first AI-Cafe of 2023 will put in the spotlight the ExaMode project on February 8th 2023 14-15h CEST.

Prof. Henning Müller will present “ExaMode: a practical approach towards using machine learning in histopathology”. The ExaMode project is funded by the European Commission to work on scalable solutions for AI in histopathology. The project thus follows a practical approach defining use cases, collecting multimodal data from hospitals and and then developing decision support tools. Aspects such as learning from limited data and multimodal learning will be addressed as well as working on aspects of explainability when building systems that are integrated into the clinical workflow.

Online regitration is free:

Article on color augmentation published in Pathology Informatics

Our work on “Data-driven color augmentation for H&E stained images in computational pathology“, led by Niccolò Marini, et al. has been published in the Elsevier Journal of Pathology Informatics.

In this work, we present a method to improve the efficiency of color augmentation methods by increasing the reliability of the augmented samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. 

The GitHub repository with the code is available online : ,

and the database including stain vectors:

Special issue on medical imaging data for digital diagnostics open for submissions

Prof. Henning Müller is co-editor of the special issue “Medical imaging data for digital diagnostics” in Nature Scientific Data, open for submissions until 20th June 2023.

This Collection presents a series of articles describing annotated datasets of medical images and video. Data are presented without hypotheses or significant analyses to support improvements such as benchmarking or improving machine learning algorithms. All medical specialities are considered and data can be derived from study participants, tissue samples, electronic health records (EHRs) or other sources. All described datasets are assessed to ensure their open availability (where possible) or secure access controls (where required) via Scientific Data‘s editorial and peer review processes.

Article on upper limb motion analysis published in Applied Sciences

Our work on “Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics“, led by Alessandro Scano, Cristina Brambilla, Henning Müller and Manfredo Atzori has been published in the Applied Sciences journal, MDPI.

Smoothness-based models describe effectively skilled motion planning. Despite smoothness being often used as a measure of motor control and to evaluate clinical pathologies, so far, a smoothness map is not available for the whole workspace of the upper limb. In this work, we provide a map of the upper limb workspace comparing four smoothness metrics: the normalized jerk, the speed metric, the spectral arc length, and the number of speed peaks.

More details in the article available online:

Scheme of the work. Upper limb movements (P2P and EXP) were performed in 5 sectors of the workspace by 15 subjects. The data were segmented in phases and the wrist kinematics and the articular angels were computed. Then, four smoothness metrics based on wrist motion were computed and smoothness was compared between sectors, mapped with respect to articular angles and in the 3D workspace for each of the smoothness metric. Finally, the correlations between pairs of metrics were computed.

Our work on taxonomy in AI interpretability highlighted in RSIPVision

A 4-pages article has been published in RSVIPVision, highlighting our work on A global taxonomy of interpretable AI, based on an excellent interview of Mara Graziani and Vincent Andrearczyk by Ralph Anzarouth.

More details on our work “A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences” are available in the paper.

This work was made possible thanks to the large collaboration of experts in various domains: Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim, Katerina Yordanova, Mor Vered, Rahul Nair, Pedro Henriques Abreu, Tobias Blanke, Valeria Pulignano, John O. Prior, Lode Lauwaert, Wessel Reijers, Adrien Depeursinge, Vincent Andrearczyk and Henning Müller.

Congratulations to dr. Pierre Fontaine !

Pierre Fontaine successfully defended his PhD work on “Extraction and analysis of image features within a radiomics work-flow applied to prediction in radiotherapy”. His thesis focused on how to optimally exploit regions of interest in medical images when building radiomics models for prognosis prediction. His list of publications is available online:

This work was co-supervised by

  • Dr Oscar Acosta, and Prof. Renaud De Crevoisier, Univ Rennes, CLCC Eugene Marquis, INSERM, LTSI – UMR 1099, F-35000 Rennes, France
  • Prof. Adrien Depeursinge, Institute of Informatics, University of Applied Sciences Western Switzerland (HESSO).
Pierre Fontaine defending his PhD thesis

Congratulations to Valentin Oreiller for his PhD !

Valentin Oreiller succesfully defended his PhD thesis on Validating and Optimizing the Specificity of Image Biomarkers for Personalized Medicine. In his work, supervised by Prof. Adrien Depeursinge (HES-SO) and, Prof. John O. Prior (CHUV), Valentin obtained excellent achievements:

  • He designed directional image operators that are Locally Rotation Invariant (LRI), based on the power spectrum and bispectrum of the circular harmonics expansion for 2D images or spherical harmonics expansion for 3D images.
  • He also tackled a crucial aspect in designing robust predictive models, that is the need for large datasets and benchmarks. An important part of his thesis is the organization of the HEad and neCK tumOR segmentation and outcome prediction in PET/CT images (HECKTOR) challenge.

His list of publications is available online:

Valentin Oreiller (right) defending his PhD thesis, supervised by Prof. Adrien Depeursinge (left) and Prof. John Prior

Article published in European Journal of Hybrid Imaging

Our work on “Reproducibility of lung cancer radiomics features extracted from data‑driven respiratory gating and free‑breathing flow imaging in [18F]‑FDG PET/CT“, Daphné Faist et al., has been published in the Springer Nature European Journal of Hybrid Imaging.

The quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [18F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging.

This study showed that 131 out of 141 radiomics features calculated on all pulmonary lesions can be used interchangeably between DDG and FB PET/CT acquisitions. Besides, radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size.

Free-breathing flow (first row) vs data-driven respiratory gating (second row) 18F-FDG PET/CT