PROGRAMME

Below you can find the preliminary 2024 programme.

Application for CME recognition will be submitted to:

  • the European Accreditation Council for Continuing Medical Education (EACCME), an institution of the European Union of Medical Specialists (UEMS). EACCME credits are recognised by the American Medical Association towards the Physician’s Recognition Award (PRA)
  • the European Board for Accreditation in Medical Physics

colour code:

Main track
Clinical track

MONDAY 24 JUNE 2024 

Topic of the day: Radiomics

09:00-09:15 Welcome and course introduction – Henry Woodruff

09:15-10:00 From Radiomics 1.0 to Multiomics: Tracing the Evolution of Radiomics – Philippe Lambin

10:00-10:45 Multi-centric radiomics – Fanny Orlhac

10:00-10:45 Network radiomics – Harini Veeraraghavan

10:45-11:15 Coffee break

11:15-12:00 Model optimization in radiomics – Martijn Starmans

11:15-12:00 Radiomics in radiotherapy – André Dekker

12:00-13:00 Lunch break

13:00-17:00 Hands-on radiomics workshop for scientists and beginners in computer rooms at UM

TUESDAY 25 JUNE 2024

Topic of the day: Deep Learning

09:00-09:15 Monday recap and discussion – Henry Woodruff

09:15-10:00 DL and clinical implementation – Bram van Ginneken

10:00-10:45 Biases and uncertainties in medical AI – Kaisar Kushibar

10:00-10:45 Using DL-based segmentation for reducing annotation time – tbc

10:45-11:15 Coffee break

11:15-12:00 Checklists for AI research – Alexander Zwanenburg

11:15-12:00 Deep learning in a clinic – Andrew Maidment

12:00-13:00 Lunch break

13:00-17:00 Hands-on deep learning workshop for scientists and beginners in computer rooms at UM

WEDNESDAY 26 JUNE 2024

Topic of the day: Generative AI

09:00-09:15 Tuesday recap and discussion – Harini Veeraraghavan

09:15-10:00 Explainable AI for Medical Imaging – Zohaib Salahuddin

10:00-10:45 Transformers for Medical Image Analysis – Harini Veeraraghavan

10:45-11:15 Coffee break

11:15-12:00 Synthetic Data – What happened? – Bram van Ginneken

11:15-12:00 Sample size calculation – Shahab Jolani

12:00-13:00 Lunch break

13:00-17:00 Hackathon with The D-Lab, Maastricht University

13:00-17:00 Design a clinical trial – part 1 – Philippe Lambin, Anshu Ankolekar

THURSDAY 27 JUNE 2024

Topic of the day: Translation

09:00-09:15 Wednesday recap and discussion – Andrew Maidment

09:15-10:00 Implementing AI in a radiotherapy centre – Wouter van Elmpt

10:00-10:45 Chat GPT & ethics – tbc

10:45-11:15 Coffee break

11:15-11:45 Closing statements – Henry Woodruff

11:45-12:15 AI4Imaging competition

12:15-13:00 Lunch break

13:00-16:30 Hackathon at The D-Lab, Maastricht University

13:00-16:30 Design a clinical trial – part 2 + pitches – Philippe Lambin, Anshu Ankolekar

COURSE PRESENTATIONS

Delegates from the 2023 edition can access the presentations here.
The folder is password protected. Please check your mailbox.

OVERVIEW OF STUDIES AVAILABLE FOR MODEL VALIDATION

Tumor siteModalityOutcomePatient number (N)
BoneScintigraphyRadiologist score1000
BrainMRIOS175
BrainMRIOS, radiologist score65
BreastMammoPathology6671
BreastCESMRadiologist score1883
BreastCTPathology220
HNCT+ HX4-PETHypoxia34
HNCECTOS, PFS311
HNFMISO-CT, FDG-PETHypoxia86
HNHX4-CT, FDG-PETHypoxia19
HNHX4-CT, FDG-PETOS, PFS, Hypoxia19
HNHX4-CT, FDG-PETOS, PFS, Hypoxia12
HNCT, FDG-PETIHC(Pimo staining)71
HNCTOS, PFS517
HNCT OS, PFS850
HNCTOS130
KidneyCTPathology136
LiverCTOS, PFS420
LiverCT, MRIOS, PFS97
LungCTOS422
LungCBCTPathology, OS, PFS71
LungCTRadiologist score1010
LungCBCTOS577
LungFAZA-CTHypoxia36
LungHX4-CT, FDG-PETOS, Hypoxia30
LungHX4-CT, FDG-PETOS, Hypoxa25
LungCTOS129
LungCTIHC53
LungCT, FDG-PETRadiologist score34
LungCTHistology89
LungCTHistology43
LungCTOS101
LungCTBiopsy139
LungCTBiopsy649
LungCTPathology805
Lung, Liver, Kidney, BoneCTLesion bounding boxes4427
ProstateMRI T2w, ADCGleason140

LATEST ARTICLES

Some interesting articles worth reading before attending the course:

Huang, S., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023). Self-supervised learning for medical image classification: A systematic review and implementation guidelines. Npj Digital Medicine, 6(1), 1-16. https://doi.org/10.1038/s41746-023-00811-0
 
Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., … & Hatamizadeh, A. (2022). Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20730-20740)
 
Ben Glocker,∗ Charles Jones, Mélanie Bernhardt, and Stefan Winzeck (2023) Algorithmic encoding of protected characteristics in chest
X-ray disease detection models https://doi.org/10.1016/j.ebiom.2023.104467

 

Alexander Kirillo, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland,…& Ross Girshick. (2023) Segment Anything https://doi.org/10.48550/arXiv.2304.02643

 

Jeroen van der Laak, Geert Litjens, and Francesco Ciompi. (2023). Deep learning in histopathology: the path to the clinic https://doi.org/10.1038/s41591-021-01343-4

 

Ning Mao, Haicheng Zhang, Yi Dai, Qin Li, Fan Lin,…& Heng Ma. (2022). Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study https://doi.org/10.1038/s41416-022-02092-y

 

Tiantian Zheng, Fan Lin, Xianglin Li, … & Ning Mao. (2023).,Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study https://doi.org/10.1016/j.eclinm.2023.101913

 

Jun Ma and Bo Wang.(2023). Segment Anything in Medical Images. https://doi.org/10.48550/arXiv.2304.12306

 

Kaiming He, Xinlei Chen,  Saining Xie, Yanghao Li, Piotr Doll´ar, Ross Girshick. (2021). Masked Autoencoders Are Scalable Vision Learners https://doi.org/10.48550/arXiv.2111.06377

 

Xueyan Mei, Zelong Liu, Ayushi Singh, Marcia Lange,… & Yang Yang. (2023). Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data. https://doi.org/10.1038/s41467-023-37720-5

 

Tian-yi Xia, Zheng-hao Zhou, Xiang-pan Meng, Jun-hao Zha,…& Sheng-hong Ju. (2023). Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model. https://doi.org/10.1148/radiol.222729