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In this work, we propose a novel method for assessing students’ behavioral engagement by representing student’s actions and their frequencies over an arbitrary time interval as a histogram of actions. This histogram and the student’s gaze are utilized as input to a classifier that determines whether the student is engaged or not. For action recognition, we use students’ skeletons to model their postures and upper body movements. To learn the dynamics of a student’s upper body, a 3D-CNN model is developed. The trained 3D-CNN model recognizes actions within every 2-minute video segment then these actions are used to build the histogram of actions. To evaluate the proposed framework, we build a dataset consisting of 1414 video segments annotated with 13 actions and 963 2-minute video segments annotated with two engagement levels. Experimental results indicate that student actions can be recognized with top-1 accuracy 86.32% and the proposed framework can capture the average engagement of the class with a 90% F1-score. 

Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slices that have polyps. We propose a colorectal polyps detection approach which consists of two cascaded stages. In the first stage, a CNN-based model is trained and validated to detect polyps in axial CT slices. To narrow down the effective receptive field of the detector neurons, the colon regions are segmented and then fed into the network instead of the original CT slice. This drastically improves the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, in the second stage, we propose a multi-view network (MVN) that classifies polyp candidates. The proposed MVN classifier is trained using sagittal and coronal views corresponding to the detected axial views. The approach is tested in 50 CTC-annotated cases, and the experimental results confirm that after the classification stage, polyps can be detected with an AUC about 95.27%. 

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides “filet”-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.