Project description
Navigating complex medical data
The sheer volume of data in health and science (from medical documents to scientific texts) makes it difficult to extract meaningful insights. The ERC-funded AUTODIAL project aims to address this challenge in dermatopathology, the field responsible for diagnosing skin diseases. By using modern natural language processing, AUTODIAL will transform scientific literature into an open knowledge graph and use multimodal analysis to define diagnostic grey zones. The project is also developing tools for interacting with large medical imaging data, improving human-computer collaboration in digital pathology. AUTODIAL’s approach standardises the understanding of complex, multimodal data and offers methods transferable to other medical and vision-based scientific fields. Ultimately, it aims to improve diagnostics and knowledge management across health sciences.
Objective
The continuous and overwhelming increase in the amount of data in health and science, including image collections, textbooks and scientific literature, renders it difficult to meaningfully understand, validate and query the available body of knowledge. Modern developments in artificial intelligence hold promise to solve these shortcomings by enabling the collection, understanding, curation, use of, as well as interaction with, large bodies of data. The goal of this project is to make this case in the explicit context of a highly specialized scientific field, dermatopathology, which is in charge of obtaining reliable diagnoses of skin diseases. We aspire to improve understanding in four different settings: 1) Unstructured real-life data: To obtain population-scale structured knowledge on the real-life burden of skin disease beyond cancer, we will use modern language models to automatically map historic unstructured pathology reports to disease entities. 2) Scientific corpora: Guidance of general-purpose language-models will be used to transform medical scientific corpora towards an open knowledge graph. 3) Cross-modality entity representation: Training a long-tail multi-modal and multi-task foundation model with enable to objectively define diagnostic grey zones and narrow them. 4) Interaction with large image data: Retrieval-, annotation-, and language-based interaction modalities will be developed into a digital microscopy viewing interface to find the most efficient avenues in human-computer interaction for the analysis of large-size medical image data.
In sum, this project employs a multifaceted approach to standardize pathways to understand large, multi-modal open-set data, and further explore further the field of human-computer interaction in highly specialized imaging professions. Results will be transferable to other medical specialties, and vision-based scientific fields in general.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- medical and health sciences clinical medicine dermatology
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences data science data mining
- medical and health sciences basic medicine pathology
- engineering and technology medical engineering diagnostic imaging
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1090 Wien
Austria
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.