PROGRAMME
Below you can find the preliminary 2021 programme.
colour code:
Main tract / Clinical tract
WEDNESDAY 8 DECEMBER 2021
Topic of the day: Radiomics
09:00-09:30 Welcome and course introduction – Philippe Lambin
09:30-10:15 Imaging challenges – Max Seidensticker
10:15-11:00 The current state of feature harmonization – Fanny Orlhac
10:15-11:00 Clinical relevance of Radiomics studies – Harini Veeraraghavan
11:00-11:30 Coffee break
11:30-12:15 Moving towards a common standard for quantitative imaging – Andrey Fedorov
11:30-12:15 MEDomics a framework for the development of AI in medicine – Olivier Morin
12:15-13:15 Lunch
13:15-17:15 Hands-on radiomics workshop for scientists
13:15-17:15 Hands-on radiomics workshop for beginners
THURSDAY 9 DECEMBER 2021
Topic of the day: Deep Learning
09:00-09:30 Wednesday recap and discussion – Henry Woodruff
09:30-10:15 What is the state of the art in DL – Bram van Ginneken
10:15-11:00 Federated Deep Learning in Healthcare – Shadi Albarqouni
11:00-11:30 Coffee break
11:30-12:15 DL for Automatic Segmentation – are we there yet? – Mark Gooding
11:30-12:15 Deep learning in a clinic – Andrew Maidment
12:15-13:15 Lunch
13:15-17:15 Hands-on deep learning workshop for scientists
13:15-17:15 Hands-on deep learning workshop for beginners
FRIDAY 10 DECEMBER 2021
Topic of the day: Synthetic Data
09:00-09:30 Thursday recap and discussion – Harini Veeraraghavan
09:30-10:15 GANS et al – the solution to lack of data? – Guang Yang
10:15-11:00 Virtual Clinical Trials – Andrew Maidment
11:00-11:30 Coffee break
11:30-12:15 Synthetic Data – can we trust it? – Bram van Ginneken
11:30-12:15 Preparatory work for the afternoon workshop – Philippe Lambin and Avishek Chatterjee
12:15-13:15 Lunch
13:15-17:15 Hackathon at The D-Lab, Maastricht University – Henry Woodruf
13:15-17:15 Design a clinical trial – Philippe Lambin and Avishek Chatterjee
SATURDAY 11 DECEMBER 2021
Topic of the day: Prospective Clinical Trials
09:00-09:30 Friday recap and discussion – Olivier Morin
09:30-10:15 Summary of the ongoing and concluded prospective clinical trials – Harini Veeraraghavan
10:15-10:45 Current state of the law regarding data – David Townend
10:45-11:15 Coffee break
11:15-11:45 Closing statements – Philippe Lambin
11:45-12:15 AI4Imaging competition
12:15-13:00 Lunch
13:00-17:00 Hackathon at The D-Lab, Maastricht University
13:00-16:00 Design a clinical trial (part 2) – Philippe Lambin and Avishek Chatterjee
COURSE PRESENTATIONS
Delegates from the 2019 edition can access the presentations here.
The folder is password protected. Please check your mailbox.
OVERVIEW OF STUDIES AVAILABLE FOR MODEL VALIDATION
| Tumor site | Modality | Outcome | Patient number (N) |
|---|---|---|---|
| Bone | Scintigraphy | Radiologist score | 1000 |
| Brain | MRI | OS | 175 |
| Brain | MRI | OS, radiologist score | 65 |
| Breast | Mammo | Pathology | 6671 |
| Breast | CESM | Radiologist score | 1883 |
| Breast | CT | Pathology | 220 |
| HN | CT+ HX4-PET | Hypoxia | 34 |
| HN | CECT | OS, PFS | 311 |
| HN | FMISO-CT, FDG-PET | Hypoxia | 86 |
| HN | HX4-CT, FDG-PET | Hypoxia | 19 |
| HN | HX4-CT, FDG-PET | OS, PFS, Hypoxia | 19 |
| HN | HX4-CT, FDG-PET | OS, PFS, Hypoxia | 12 |
| HN | CT, FDG-PET | IHC(Pimo staining) | 71 |
| HN | CT | OS, PFS | 517 |
| HN | CT | OS, PFS | 850 |
| HN | CT | OS | 130 |
| Kidney | CT | Pathology | 136 |
| Liver | CT | OS, PFS | 420 |
| Liver | CT, MRI | OS, PFS | 97 |
| Lung | CT | OS | 422 |
| Lung | CBCT | Pathology, OS, PFS | 71 |
| Lung | CT | Radiologist score | 1010 |
| Lung | CBCT | OS | 577 |
| Lung | FAZA-CT | Hypoxia | 36 |
| Lung | HX4-CT, FDG-PET | OS, Hypoxia | 30 |
| Lung | HX4-CT, FDG-PET | OS, Hypoxa | 25 |
| Lung | CT | OS | 129 |
| Lung | CT | IHC | 53 |
| Lung | CT, FDG-PET | Radiologist score | 34 |
| Lung | CT | Histology | 89 |
| Lung | CT | Histology | 43 |
| Lung | CT | OS | 101 |
| Lung | CT | Biopsy | 139 |
| Lung | CT | Biopsy | 649 |
| Lung | CT | Pathology | 805 |
| Lung, Liver, Kidney, Bone | CT | Lesion bounding boxes | 4427 |
| Prostate | MRI T2w, ADC | Gleason | 140 |
LATEST ARTICLES
Some interesting articles worth reading before attending the course:
“Radiomics: from qualitative to quantitative imaging”, Rogers, W. et al. (2020), The British Journal of Radiology, 93 (1108)
doi: 10.1259/bjr.20190948
“Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)”, Zerka et al. (2020), IEEE Access 8, 183939-183951
Doi: 10.1109/ACCESS.2020.3029445
“The effects of in-plane spatial resolution on CT-based radiomic features’ stability with and without ComBat harmonization”, Ibrahim et al. (2021), Cancers 13 (8), 1848
Doi: 10.3390/cancers13081848
“Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework”, Ibrahim et al. (2021), Methods, 188, 20-29
doi: 10.1016/j.ymeth.2020.05.022
“The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset”, Ibrahim et al. (2021), PloS one 16 (5), e0251147
Doi: 10.1371/journal.pone.0251147