About the lab

The MedGIFT project started at the medical faculty of the University of Geneva, Switzerland in 2002 and is since 2007 located in the Institute for Business Information Systems at the HES-SO in Sierre (Valais), Switzerland. The name stems originally from the use of the GNU Image Finding Tool (GIFT) for medical applications. Over the years the GIFT has been used less frequently and a large set of tools and applications have been developed to advance the field of medical visual information retrieval. All developed tools are open source and can be requested by email. Some very old tools might not be available anymore. A very strong collaboration with medical informatics and the University Hospitals and University of Geneva, Switzerland continues to keep the group activities in medical informat

Featured research (30)

This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction, we used a combination of deep representation learning and a rule-based approach. Our best model for subtask 1 ranks fourth with an F1-score of 65.77%. It shows moderate benefit from en-sembling models pre-trained on independent categories. The results for subtask 2 warrant further investigation for improvement.
Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency. Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model. Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives. Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g. variations in size and appearance, while discarding misleading features such as staining differences. Our results on breast lymph node tissue show significantly improved generalization in the detection of tumorous tissue, with best average AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. This result is a starting point towards building interpretable multi-task architectures that are robust to data heterogeneity. Our code is available at</a
Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML.
Objective The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing. Methodology We re-purpose more than 127 medical and nonmedical ontologies and expert-generated rules to obtain multiple noisy labels for PICO entities in the evidence-based medicine (EBM)-PICO corpus. These noisy labels are aggregated using simple majority voting and generative modeling to get consensus labels. The resulting probabilistic labels are used as weak signals to train a weakly supervised (WS) discriminative model and observe performance changes. We explore mistakes in the EBM-PICO that could have led to inaccurate evaluation of previous automation methods. Results In total, 4081 randomized clinical trials were weakly labeled to train the WS models and compared against full supervision. The models were separately trained for PICO entities and evaluated on the EBM-PICO test set. A WS approach combining ontologies and expert-generated rules outperformed full supervision for the participant entity by 1.71% macro-F1. Error analysis on the EBM-PICO subset revealed 18–23% erroneous token classifications. Discussion Automatic PICO entity extraction accelerates the writing of clinical systematic reviews that commonly use PICO information to filter health evidence. However, PICO extends to more entities—PICOS (S—study type and design), PICOC (C—context), and PICOT (T—timeframe) for which labelled datasets are unavailable. In such cases, the ability to use weak supervision overcomes the expensive annotation bottleneck. Conclusions We show the feasibility of WS PICO entity extraction using freely available ontologies and heuristics without manually annotated data. Weak supervision has encouraging performance compared to full supervision but requires careful design to outperform it.

Lab head

Henning Müller
  • Information systems
About Henning Müller
  • Henning Müller currently works at the Information systems Institute, HES-SO Valais-Wallis in Sierre, Switzerland. Henning does research in Artificial Intelligence, Medical Image analysis and information retrieval.

Members (16)

Adrien Depeursinge
  • HES-SO Valais-Wallis
Manfredo Atzori
  • HES-SO Valais-Wallis
Cristina Simon-Martinez
  • HES-SO Valais-Wallis
Vincent Andrearczyk
  • HES-SO Valais-Wallis
Roger Schaer
  • HES-SO Valais-Wallis
Ivan Eggel
  • HES-SO Valais-Wallis
Mara Graziani
  • IBM Research Europe and Hes-so Valais
Obioma Pelka
  • University of Applied Science and Arts Dortmund
Pierre-Alexandre Poletti
Pierre-Alexandre Poletti
  • Not confirmed yet
Anastasia Rozhyna
Anastasia Rozhyna
  • Not confirmed yet

Alumni (6)

Alba García Seco de Herrera
  • University of Essex
Yashin Dicente Cid
  • Roche Diagnostics
Dimitrios Markonis
  • HES-SO Valais-Wallis