Year: 2021

Award given at the Practical Big Data Workshop 2021 put on by the American Association of Physicists in Medicine.

The paper is entitled “Correlation of In-Vivo Imaging with Histopathology: A Review”.

https://doi.org/10.1016/j.ejrad.2021.109964

Poster accepted into the Practical Big Data Workshop 2021 by AAPM.

Detecting Out-of-Distribution Images for Active Learning using a Generative Adversarial Network (StyleGAN2)

Purpose: To detect outliers from a medical imaging distribution used to train deep learning models. The proposed method can identify Computed Tomography (CT) scans that would make a clinically deployed segmentation model fail.

Methods: A cohort of 143 patients with 147 non-contrast enhanced abdominal CTs (97 training, 50 test) was used. A StyleGAN2 network, a state-of-the-art high-resolution generative model that uses a style-based generator and backpropagation to encode, was trained to reconstruct slices. Data was preprocessed with windowing, masking, and conversion to 512×512 PNG images. The network’s generative quality was measured with the Fréchet Inception (FID) and Wasserstein (WD) distances. Slice reconstructions from the test CTs with a learned perceptual image patch similarity score (compares VGG network feature representations) over 0.1 were classified as out-of-distribution.

Results: Randomly generated slices had FID and WD metric values of 8.77 and 0.10, respectively. All test images on which a segmentation model failed (the model had a Dice coefficient of 0.96 on test CTs) were classified as out-of-distribution.

Conclusion: A paradigm was optimized to predict when a clinically deployed segmentation model would fail with a 100% success rate. The paradigm could be further used to create heterogenous imaging datasets and prioritize images for labeling.

• Advised a student who built a glioblastoma detector on MRI scans using a pre-trained CNN
• Guided another student who built an arrythmia detector on ECG signals using a pre-trained ResNet
• Gave a talk on deep learning in medical imaging to ~300 high school students

I had two abstracts for oral presentations accepted to AAPM 2021!

Awarded Worldwide Innovations in Medical Physics scholarship at the Winter Institute of Medical Physics. View poster here.

Woodland, M., Hsiung, M., Matheson, E., Safsten, A., Greenwood, J., Halverson, D.M., Howell, L.L., “Analysis of the Rigid Motion of a Developable Conical Mechanism,” Journal of Mechanisms and Robotics, https://doi.org/10.1115/1.4050294, Vol 13, 031106-1 to 031106-8, 2021.

Awarded semi-finalist in the Worldwide Innovations in Medical Physics scholarship competition at the Winter Institute of Medical Physics (WIMP).