Evolvable Deep Features

Temporal lobe injury (TLI), a form of nervous system damage in the brain, is a major neurological complication after radiation therapy (RT). TLI must be highly valued because of the irreversible brain injury. This paper aims to develop a predictive pipeline, called deep longitudinal feature representations (DLFR), to detect TLI at the presymptomatic stage accurately via the learning of effective deep longitudinal feature representations. DLFR characterizes high-level information and developmental changes within and across subjects The DLFR consists of four components: (i) extraction of deep features from a pretrained ResNet50 model; (ii) compression of learned highly representative features by the global max pooling; (iii) fusion of deep longitudinal features for the fully use of all follow-up data; (iv) random forest-based prediction of the diagnostic status. In total, 244 nasopharyngeal carcinoma patients before and after RT with a follow-up period of 0 ∼ 9 years were included for analysis. All patients were divided into four different latency groups, and the current latency was used for training to predict the diagnostic status of the next latency. The AUCs of the predicted three different latency groups using DLFR were 0.64 ± 0.11, 0.76 ± 0.10, and 0.88 ± 0.05, while those of radiomics features were 0.56 ± 0.06, 0.63 ± 0.03, and 0.53 ± 0.04, and those of histogram of oriented gradients features were 0.60 ± 0.09, 0.52 ± 0.03, and 0.58 ± 0.06. Most importantly, the AUCs of the predicted three different latency groups for white matter regions were 0.66 ± 0.10, 0.80 ± 0.09, and 0.78 ± 0.09. Our proposed method can dynamically detect TLI at the presymptomatic stage, which can enable the administration of preventive neurological intervention.

Section: Discussionmentioning

confidence: 99%

Deep Longitudinal Feature Representations for Detection of Postradiotherapy Brain Injury at Presymptomatic Stage

Zhong

Zhang

et al. 2020

“…We additionally mine batch-hard positive and negative examples and introduce respective positive-sample and negative-sample loss functions to further supplement the network in separating identity classes. [47] for machine learning classification. However, re-ID differs significantly from standard classification tasks in several ways: i) our model exploits metric learning rather than using a classification loss, ii) testing identities in re-ID do not appear in training, whereas all classes appear in training for regular classification tasks, iii) the evaluation metric is based on information retrieval rather than the classification accuracy.…”

Section: B Tripletsmentioning

confidence: 99%

Unifying Person and Vehicle Re-Identification

Organisciak

Sakkos

et al. 2020

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.

“…Since the extracted feature channels from different layers are too large to fit our network effectively, we need to adopt deep feature selection. Recently, Nalepa et al [57] introduces a method for automatic deep feature selection, which combines a genetic algorithm with deep learning to reduce the number of feature channels. We think this method is novel, but it adds a higher level of complexity, similar to that encountered in the traditional genetic algorithm, which is inconvenient when we test the performance of our algorithm.…”

Section: A Cnn Featuresmentioning

confidence: 99%

Deep Ensemble Object Tracking Based on Temporal and Spatial Networks

Chen

2020

In recent years, correlation filtering and deep learning have achieved good performance in object tracking. Correlation filtering is an efficient and real-time method because its formula provides a fast solution in the Fourier domain, but it does not benefit from end-to-end training. Although deep learning is an effective method for learning object representations, training deep networks online with one or a few examples is challenging. To address these problems, we propose a deep ensemble object tracking algorithm that fuses temporal and spatial information to improve algorithm precision and robustness. The framework of our algorithm includes four aspects: feature extraction, a baseline network, a branch network and adaptive ensemble learning. Feature extraction extracts the general object representation. The baseline network integrates feature extraction and a correlation filtering algorithm into a convolutional neural network for end-to-end training. The branch network is composed of a temporal network and a spatial network. The temporal and spatial networks capture the object temporal and spatial information and further refine the object position. Our algorithm only needs an initial frame to train all networks. Adaptive ensemble learning compensates for the object information deficiency and improves tracking accuracy. Many experiments on tracking benchmark datasets demonstrate that our algorithm performs favourably compared with state-ofthe-art tracking algorithms. INDEX TERMS Object tracking, feature extraction, baseline network, temporal and spatial networks, adaptive ensemble learning.