| Question 1: Historically, what types of architecture excels at modeling sequential information? |
| Reference: | Liu, Pengfei, Xipeng Qiu, and Xuanjing Huang. "Recurrent neural network for text classification with multi-task learning." arXiv preprint arXiv:1605.05101 (2016). |
| Choice A: | Recurrent Neural Networks |
| Choice B: | Convolutional Neural Networks |
| Choice C: | Fully Connected Networks |
| Choice D: | Relational Networks |
| Question 2: Which type of architecture excels at pixel wise classification? |
| Reference: | Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015. |
| Choice A: | ResNet |
| Choice B: | DenseNet |
| Choice C: | VGG |
| Choice D: | Encoder-Decoder Networks |
| Question 3: Which is not a part in the process of patient-specific QA using machine learning? |
| Reference: | G Valdes, R Scheuermann, CY Hung, A Olszanski, M Bellerive, TD Solberg, “A mathematical framework for virtual IMRT QA using machine learningâ€, Medical Physics, 2016; 43(7):4323-4334 |
| Choice A: | Extracting features using scripts to read IMRT beams from TPS database |
| Choice B: | Building models by calculating all plan complexity metrics affecting passing rate |
| Choice C: | Collecting IMRT QA data for building and re-training virtual QA models |
| Choice D: | Measuring treatment machine outputs |
| Question 4: Which of the following statement on Virtual IMRT QA is not true? |
| Reference: | G Valdes, MF Chan, SB Lim, R Scheuermann, JO Deasy, and TD Solberg, “IMRT QA using machine learning: A multi-institutional validationâ€, Journal of Applied Clinical Medical Physics, 2017; 18:5:279-284 |
| Choice A: | Different TPS dose calculation model needs a different virtual IMRT QA model |
| Choice B: | Virtual IMRT QA is disease-site dependent |
| Choice C: | Virtual IMRT QA can accurately predict passing rate for any plan |
| Choice D: | Virtual IMRT QA model needs to be refined for different detectors & energies |
| Question 5: Multi-atlas deformable image registration algorithms have been shown to be very effective (i.e. clinically acceptable for the majority of patients) for delineating: |
| Reference: | McCarroll et al, Retrospective validation and clinical implementation of automated contouring of organs at risk in the head and neck: A step toward automated radiation treatment planning for low- and middle-income countries, Journal of Global Oncology,4, 1-11, 2018 |
| Choice A: | Anatomical structures (e.g. parotid) |
| Choice B: | High-risk CTVs |
| Choice C: | Microscopic spread of disease |
| Choice D: | GTVs |
| Question 6: Deep learning techniques have been shown to be capable of delineating high-risk CTVs in head/neck patients, with the mean agreement between the automatic contours and manual contours of: |
| Reference: | Cardenas et al, Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function, IJROBP 101(2), 468-478, 2018 |
| Choice A: | <1 mm |
| Choice B: | 2-4 mm |
| Choice C: | 5-15 mm |
| Choice D: | 6-20 mm |
