Auguste Koh, Amy Tai

29 September, 2023, 11:30 am, EC4-2101A

*** 11:30am Presenter: August Koh ***

Two projects on the topic of object relationships will be presented during this seminar:

1. Distances between vehicles: In order to improve road traffic safety, there is interest in first identifying road segments and intersections where there is a high risk of accidents. Identifying these areas can be done by referring to accident statistics; however, a more proactive identification of high-risk road segments and intersections could be done by collecting other risk metrics before the occurrence of many accidents. As such, this project, in collaboration with Miovision, is on the topic of predicting distances between vehicles in order to identify close calls, which are presumably much more common than accidents, from wide-angle footage of road traffic. This seminar will present work done toward the creation of a dataset and a proposed architecture for predicting distances between vehicles.

2. Relative arrangement of two objects: In the context of the bin picking problem, it is desirable for a bin-picking system (robotic arm, sensor(s), and model) to analyze the scene and pick objects quickly. This can be particularly challenging in cluttered scenes where (i) the target object in the scene is partially occluded by other objects, (ii) non-target objects might physically interfere with the robotic arm and cause a grasp attempt to fail, (iii) grasping the target object might cause other objects that were physically supported by it to be moved and to potentially fall out of the bin. A model to predict object relationships was developed for use on the MetaGraspNet dataset, which presents at least three distinct difficulties:

reciprocally occluding objects, occluded relationship boundaries, and small overlap areas. A graph-network model with the aim of addressing these difficulties will be presented.

*** 12:pm Presenter: Amy Tai ***

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25\% of all new female cancer cases. As such, there has been immense research and progress on improving screening and clinical support for breast cancer. In this paper, we investigate enhancing clinical support for breast cancer with deep learning models using a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDIs). More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features for grade and post-treatment response prediction. As the first study to learn CDIs-centric radiomic sequences within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, the approach to leverage volumetric deep radiomic features for breast cancer can be further extended to other applications of CDIs in the cancer domain to further improve clinical support.