D5.3 Deep Learning Master model and Radiomic Signatures

Mar 12, 2024 | Deliverables

Deliverable 5.3, led by partner FCHAMPALIMAUD, contains the work performed by the ProCAncer-I
consortium on master models using radiomics and deep learning techniques. ’Master models’ — models
which can act as a foundation for other models — were developed for radiomics for all relevant use cases
(UC2, UC3, UC5, UC6, UC7a, UC7b and UC8) through the development of consistent and robust pipelines,
while deep learning was used only for UC1, UC2 and UC5 due to its more demanding data requirements.
Radiomics master models were developed by three partners (FCHAMPALIMAUD, FORTH and CNR), while
deep learning master models were developed and investigated by six different partners (FCHAMPALIMAUD,
CNR, FORTH, ADVANTIS, FPO, QUIBIM). Through this approach, several aspects of deep learning models
were investigated and consistent approaches and trends were identified. We finally note that the concept of
a ’master model’ is similar to that of a foundation model; in that light, this deliverable reflects that insight.
We describe the work in terms of foundation models and provide an overview of all experiments performed
to arrive at foundation models.

Highlights

Third Dissemination Event of the ProCAncer-I Project in Athens

Third Dissemination Event of the ProCAncer-I Project in Athens

ProCAncer-I will be organising the 3rd Dissemination Event at the 21st IEEE International Symposium on Biomedical Imaging, which will be held in Athens, Greece, May 27-30, 2024. ProCAncer-I will organise the Workshop “Integrating imaging Data and AI models for...

ProCAncer-I at the ECR 2024 in Vienna

ProCAncer-I at the ECR 2024 in Vienna

A great congress once more! Artificial Intelligence (AI) took the spotlight at the European Radiology Congress (ECR) held at the Austria Center Vienna from February 28 to March 3. Under the theme of 'Next Generation Radiology,' cutting-edge technologies were...

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