Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Once model) being one of the state-of-the-art in DNN-based object detection methods in terms of both speed and accuracy. Although YOLOv2 can achieve real-time performance on a powerful GPU, it still remains very challenging for leveraging this approach for real-time object detection in video on embedded computing devices with limited computational power and limited memory. In this paper, we propose a new framework called Fast YOLO, a fast You Only Look Once framework which accelerates YOLOv2 to be able to perform object detection in video on embedded devices in a realtime manner. First, we leverage the evolutionary deep intelligence framework to evolve the YOLOv2 network architecture and produce an optimized architecture (referred to as O-YOLOv2 here) that has 2.8X fewer parameters with just a ∼2% IOU drop. To further reduce power consumption on embedded devices while maintaining performance, a motion-adaptive inference method is introduced into the proposed Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2 based on temporal motion characteristics. Experimental results show that the proposed Fast YOLO framework can reduce the number of deep inferences by an average of 38.13%, and an average speedup of ∼3.3X for objection detection in video compared to the original YOLOv2, leading Fast YOLO to run an average of ∼18FPS on a Nvidia Jetson TX1 embedded system.

Abstract. Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data. In this study, we realize these custom radiomic sequencers as deep convolutional sequencers using a deep convolutional neural network learning architecture. To illustrate the prognostic power and effectiveness of the radiomic sequences produced by the discovered sequencer, we perform cancer prediction between malignant and benign lesions from 97 patients using the pathologically-proven diagnostic data from the LIDC-IDRI dataset. Using the clinically provided pathologically-proven data as ground truth, the proposed framework provided an average accuracy of 77.52% via 10-fold cross-validation with a sensitivity of 79.06% and specificity of 76.11%, surpassing the state-ofthe art method.

Neural network models have shown state of the art performance inseveral applications. However it has been observed that they aresusceptible to adversarial attacks: small perturbations to the inputthat fool a network model into mislabelling the input data. Theseattacks can also transfer from one network model to another, whichraises concerns over their applicability, particularly when there areprivacy and security risks involved. In this work, we conduct a studyto analyze the effect of network architectures and weight initial-ization on the robustness of individual network models as well astransferability of adversarial attacks. Experimental results demon-strate that while weight initialization has no affect on the robustnessof a network model, it does have an affect on attack transferabilityto a network model. Results also show that the complexity of anetwork model as indicated by the total number of parameters andMAC number is not indicative of a network’s robustness to attackor transferability, but accuracy can be; within the same architec-ture, higher accuracy usually indicates a more robust network, butacross architectures there is no strong link between accuracy androbustness.