Program Information

Machine Learning in Medical Physics

X Tang

H Lin

L Xing

T Kadir

X Tang¹*, H Lin²*, L Xing³*, T Kadir⁴*, (1) Memorial Sloan Kettering Cancer Center, West Harrison, NY, (2) Rensselaer Polytechnic Institute, Troy, NY, (3) Stanford University School of Medicine, Stanford, CA, (4) Mirada Medical, Oxford, UK

Presentations

7:30 AM : An overview of Machine Learning in Medical Physics - X Tang, Presenting Author
8:00 AM : Deep Learning in Medical Physics—lessons we learned - H Lin, Presenting Author
8:30 AM : Machine Learning for image analysis and treatment planning - L Xing, Presenting Author
9:00 AM : Deep-learned and practical: implementing machine learning techniques in the real world - T Kadir, Presenting Author

MO-AB-FS2-0 (Monday, July 31, 2017) 7:30 AM - 9:30 AM Room: Four Seasons 2

Machine learning has been implemented in many medical physics applications. This session provides an overview of the role of machine learning in medical physics. We will cover the basics of common machine learning algorithms including both supervised and unsupervised learning algorithms and their pros, cons, and future trends. Then we will discuss different performance evaluation methods. Detailed discussion will be conducted on various medical physics applications, including segmentation/contouring, image guided radiation therapy, treatment planning, motion management, and treatment response modeling and outcome prediction.

Deep learning is a branch of machine learning algorithms which has become a growing trend in data mining and computer vision area. We will go through its basic theory, potential applications and challenges that are unique to the medical physics domain.

Finally we will discuss some of the practical challenges encountered when bringing machine learning to the clinic including how machine learning techniques are implemented, validated and deployed by industry.

Learning Objectives:
1. Learn the common machine learning algorithms and performance evaluation methods.
2. Machine learning applications in medical physics.
3. Learn what Deep Learning means to medical physics, and what the current practices and challenges are.
4. Learn how machine learning techniques are utilized and implemented in industry.

Handouts

• 127-35483-419554-127018.pdf (X Tang)

• 127-35484-419554-125539.pdf (L Xing)

• 127-35485-419554-126473.pdf (T Kadir)

• 127-36916-419554-130797.pdf (H Lin)

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