AIR — Winter 2024/25

Mon, 14 Oct 2024 00:00:00 +0000

Seminar (2 SWS): Artificial Intelligence in Radiology: Applications and Research (Artifizielle Intelligenz in der Radiologie: Anwendungen und Forschung)

Lecturers:

Tutors: ,

Time: November 15 – Jan 31 (every Friday, ~~11:30 AM - 1:00 PM~~ 16:00 - 17:30)

Format: Online (Zoom Meeting) with potential face-to-face meetings, if participants interested! Link was provided via email.

Language: English

eCampus: to eCampus

Description

The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on Artificial Intelligence in Radiology: Applications and Research. This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.

Course Objectives

Upon completing this seminar, students will be able to:

Grasp deep learning concepts and their specific applications in medical imaging.
Analyze advanced image analysis tasks, including classification, detection, segmentation, and disease progression modeling.
Evaluate ethical and regulatory considerations in the deployment of AI within healthcare.
Discuss the future potential of AI-driven solutions in personalized medicine and patient care.

Target Audience

This seminar is designed for medical students at the Faculty of Medicine, University Hospital Bonn, who are keen on leveraging AI technologies to enhance their medical practice, specifically within diagnostic imaging.

Prerequisites and Desired Knowledge

Prerequisites: Familiarity with fundamental medical imaging techniques and an interest in AI applications.
Desired: Understanding of medical diagnostics and clinical data interpretation.

Seminar Topics and Schedule (Tentative)

Date	Topic	Description	Materials
Nov 22	Deep Learning Foundations	Introduction to deep learning concepts specific to medical imaging.	Meskó, Bertalan, and Marton Görög. “A short guide for medical professionals in the era of artificial intelligence.” NPJ digital medicine 3.1 (2020): 126.
Nov 29	Detection and Classification in Medical Imaging	Exploring detection and classification techniques in diagnosing diseases from medical images.	Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88. , Chen, Xuxin, et al. “Recent advances and clinical applications of deep learning in medical image analysis.” Medical image analysis 79 (2022): 102444. ,
Dec 6	Segmentation	Deep learning methods for segmenting key structures in medical images.	Tajbakhsh, Nima, et al. “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.” Medical image analysis 63 (2020):101693.
Dec 13	Causality and Bias	Causality and Bias in deep learning with medical imaging.	Castro, Daniel C., Ian Walker, and Ben Glocker. “Causality matters in medical imaging.” Nature Communications 11.1 (2020): 3673. , Jones, Charles, et al. “A causal perspective on dataset bias in machine learning for medical imaging.” Nature Machine Intelligence 6.2 (2024): 138-146
Dec 20	~~Uncertainity Quantification~~	Moved to the next year due to holiday season
Jan 10	Q&A Session	Students should prepare questions for their assigned papers and discuss them with the tutors
Jan 17	Uncertainity Quantification	Quanitifying Uncertainty in Deep Learning Models	Huang, Ling, et al. “A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods.” Medical Image Analysis (2024): 103223.
Jan 24	Student Paper Presentations (1)	Student presentations.
Jan 31	Student Paper Presentations (2) and Course Wrap-Up	Wrap-up, feedback, and discussion on future AI applications in medical practice.

Learning Format

One of the seminar’s unique teaching concepts is “Teach It to Yourself.” Participants (limited to 20 partisipants) will present selected chapter and papers, engage in discussions, and critically analyze case studies on deep learning applications in medical imaging.

Registration (closed)

Interested in this seminar, please register via email to and

References

[1] Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88.
[2] Shen, Dinggang, Guorong Wu, and Heung-Il Suk. “Deep learning in medical image analysis.” Annual review of biomedical engineering 19.1 (2017): 221-248.
[3] Chen, Xuxin, et al. “Recent advances and clinical applications of deep learning in medical image analysis.” Medical image analysis 79 (2022): 102444.
[4] Tajbakhsh, Nima, et al. “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.” Medical image analysis 63 (2020):101693.
[5] Wang, Ge, Jong Chul Ye, and Bruno De Man. “Deep learning for tomographic image reconstruction.” Nature machine intelligence 2.12 (2020): 737-748.
[6] Ben Yedder, Hanene, Ben Cardoen, and Ghassan Hamarneh. “Deep learning for biomedical image reconstruction: A survey.” Artificial intelligence review 54.1 (2021): 215-251.
[7] Huang, Ling, et al. “A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods.” Medical Image Analysis (2024): 103223.
[8] Castro, Daniel C., Ian Walker, and Ben Glocker. “Causality matters in medical imaging.” Nature Communications 11.1 (2020): 3673.
[9] Jones, Charles, et al. “A causal perspective on dataset bias in machine learning for medical imaging.” Nature Machine Intelligence 6.2 (2024): 138-146.
[10] Thirunavukarasu, Arun James, et al. “Large language models in medicine.” Nature medicine 29.8 (2023): 1930-1940.
[11] Zhang, Jingyi, et al. “Vision-language models for vision tasks: A survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

AAI6430 — Winter 2024/25

Tue, 01 Oct 2024 00:00:00 +0000

AI for Medical Diagnosis and Prediction

Lecturers:

Tutors: TBA

Time: March - April 2025 (tentative)

Course Description

This is an 8-week course designed for non-computer science students at the Lebanese American University. The course equips students with practical skills to implement AI in the field of medical diagnosis and prediction. Students will explore techniques like medical image classification, segmentation, object detection, and reconstruction, as well as time-series data analysis for medical forecasting. Additionally, they will learn about weakly-, semi-, and self-supervised learning, and how to tackle fairness and robustness challenges in healthcare AI.

Course Objectives

By the end of this course, students will:

Understand the role of AI in medical diagnosis and prediction.
Apply AI techniques to medical image analysis and time-series forecasting.
Explore weakly-, semi-, and self-supervised learning in healthcare.
Analyze ethical implications, fairness, and robustness in healthcare AI models.
Gain hands-on experience with coding medical AI solutions.

Target Audience

This course is tailored for non-computer science students who have taken introductory courses in Machine Learning and Deep Learning. The students are expected to have an interest in applying AI to medical applications but do not require deep knowledge of computer science concepts.

Course Schedule (12 hours per week)

Week	Lecture (3 hrs)	Description	Practical Sessions (3 hrs)
Week 1	Introduction to AI in Healthcare	Overview of AI in healthcare, challenges, and opportunities.	Image classification using CNNs on medical datasets.
Week 2	Medical Image Segmentation	Explore AI segmentation techniques like U-Net.	Implement U-Net for medical image segmentation.
Week 3	Object Detection in Medical Imaging	Object detection models and challenges in medical images.	Implement YOLO/Faster R-CNN on medical datasets.
Week 4	Medical Image Reconstruction & Time-Series Data	AI techniques for image reconstruction and medical time-series.	Time-series classification using RNN/LSTM models.
Week 5	Time-Series Forecasting & Weakly Supervised Learning	Forecasting techniques and weakly supervised learning in healthcare.	Apply forecasting models and weakly labeled data.
Week 6	Semi-Supervised & Self-Supervised Learning	Explore semi- and self-supervised learning.	Train models with limited labels using contrastive learning.
Week 7	Fairness & Robustness in AI for Healthcare	Ethical AI, fairness, and robustness in healthcare AI models.	Analyze bias and robustness in healthcare datasets.
Week 8	Advanced Applications & Final Project Presentation	Explore advanced AI applications and wrap-up project presentations.	Student presentations and peer review of final projects.

Course Materials

Lecture slides and readings will be provided weekly.
Jupyter notebooks for hands-on labs.

Evaluation and Projects

Students will be evaluated through a combination of:

Predefined Projects: Students will work on predefined medical AI projects, implementing techniques learned throughout the course.
MCQs: Quizzes covering theoretical aspects of AI and its applications in healthcare.
Project Presentations: Final presentations of projects in Week 8.
Peer Reviews: Students will review each other’s projects for additional feedback and learning.

Prerequisites

Students should have a basic understanding of machine learning and deep learning, as well as some experience with Python programming.

Contact Information

For any inquiries, please contact the instructor at .

WS2425 |

AIR — Winter 2024/25

Seminar (2 SWS): Artificial Intelligence in Radiology: Applications and Research (Artifizielle Intelligenz in der Radiologie: Anwendungen und Forschung)

Description

Course Objectives

Target Audience

Prerequisites and Desired Knowledge

Seminar Topics and Schedule (Tentative)

Learning Format

Registration (closed)

References

AAI6430 — Winter 2024/25

AI for Medical Diagnosis and Prediction

Course Description

Course Objectives

Target Audience

Course Schedule (12 hours per week)

Course Materials

Evaluation and Projects

Prerequisites

Contact Information