An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.Comment: 6 pages, 10 figures,2018 International Conference on Pattern Recognitio

Convolutional Neural Network in Pattern Recognition

Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier. As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification. Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN. The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics

ELUCID - Exploring the Local Universe with reConstructed Initial Density field III: Constrained Simulation in the SDSS Volume

A method we developed recently for the reconstruction of the initial density field in the nearby Universe is applied to the Sloan Digital Sky Survey Data Release 7. A high-resolution N-body constrained simulation (CS) of the reconstructed initial condition, with $3072^3$ particles evolved in a 500 Mpc/h box, is carried out and analyzed in terms of the statistical properties of the final density field and its relation with the distribution of SDSS galaxies. We find that the statistical properties of the cosmic web and the halo populations are accurately reproduced in the CS. The galaxy density field is strongly correlated with the CS density field, with a bias that depend on both galaxy luminosity and color. Our further investigations show that the CS provides robust quantities describing the environments within which the observed galaxies and galaxy systems reside. Cosmic variance is greatly reduced in the CS so that the statistical uncertainties can be controlled effectively even for samples of small volumes.Comment: submitted to ApJ, 19 pages, 22 figures. Please download the high-resolution version at http://staff.ustc.edu.cn/~whywang/paper

The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection

Deep learning technology has been widely used in edge computing. However, pandemics like covid-19 require deep learning capabilities at mobile devices (detect respiratory rate using mobile robotics or conduct CT scan using a mobile scanner), which are severely constrained by the limited storage and computation resources at the device level. To solve this problem, we propose a three-tier architecture, including robot layers, edge layers, and cloud layers. We adopt this architecture to design a non-contact respiratory monitoring system to break down respiratory rate calculation tasks. Experimental results of respiratory rate monitoring show that the proposed approach in this paper significantly outperforms other approaches. It is supported by computation time costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively. And the computation time costs of our three-tier architecture are less than that of edge+cloud architecture and cloud architecture. Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future

Effect of preemptive local injection of ropivocaine with dexmedetomidine on mirror pain in rats and its mechanism

AbstractObjectiveTo observe the effect of preemptive local injection of ropivocaine with dexmedetomidine on activation of glial cells and on the mirror pain in rats and its mechanism.MethodsA total of 48 adult male Sprague-Dawley rats (weighing 180 g–220 g) were included in the study and randomized into 3 groups, Group S, Group R, and Group RD1. A rat model of persistent postoperative pain evoked by skin/muscle incision and retraction was established in the three groups. Before procedures and nerve extraction, Group S (n = 16) was injected 0.9% saline locally; Group R (n = 16) was injected 0.5% ropivocaine locally, and Group RD1 (n = 16) was injected 0.5% ropivocaine in combined with 1 μg dexmedetomidine locally. After the model being established in the three groups, 8 rats were used for behavior test until 28 d, and dorsal root ganglions (DRGs) of the other 8 rats were harvested on the 3rd day after surgery. Immunofluorescent and transmission electron microscopy were used to observe the activation of glial cells in DRG, and the behavior test results in the three groups were compared.ResultsThe results showed that mechanical pain threshold in ipsilateral hind-paws of the Group S, Group R, Group RD1 animals dropped to (3.640 ± 1.963) g, (5.827 ± 1.204) g, (7.482) ± 1.412 g at 3 d respectively; while in contralateral paws dropped to (7.100 ± 1.789) g, (17.687 ± 1.112) g, (16.213 ± 1.345) g on the 3 d respectively. Immunofluorescent showed that the glial cells were activated in bilateral side DRG after surgery in 3 groups, but ipsilateral paws expressed more active glial cells than contralateral paws. Transmission electron microscopy showed that mitochondria swelling/vacuolization and lysosomes were more obvious in ipsilateral paws than contralateral paws, but Group RD1 formula could reduce glial cells activity, mitochondria swelling/vacuolization and the amount of lysosomes.ConclusionsLocal injection of ropivocaine and/or dexmedetomidine can effectively inhibit the activation of glial cells in DRG, mitigate the pathological changes of neuron in DRG and reduce mirror image pain

Full-sky ray-tracing simulation of weak lensing using ELUCID simulations: exploring galaxy intrinsic alignment and cosmic shear correlations

The intrinsic alignment of galaxies is an important systematic effect in weak-lensing surveys, which can affect the derived cosmological parameters. One direct way to distinguish different alignment models and quantify their effects on the measurement is to produce mocked weak-lensing surveys. In this work, we use full-sky ray-tracing technique to produce mock images of galaxies from the ELUCID $N$-body simulation run with the WMAP9 cosmology. In our model we assume that the shape of central elliptical galaxy follows that of the dark matter halo, and spiral galaxy follows the halo spin. Using the mocked galaxy images, a combination of galaxy intrinsic shape and the gravitational shear, we compare the predicted tomographic shear correlations to the results of KiDS and DLS. It is found that our predictions stay between the KiDS and DLS results. We rule out a model in which the satellite galaxies are radially aligned with the center galaxy, otherwise the shear-correlations on small scales are too high. Most important, we find that although the intrinsic alignment of spiral galaxies is very weak, they induce a positive correlation between the gravitational shear signal and the intrinsic galaxy orientation (GI). This is because the spiral galaxy is tangentially aligned with the nearby large-scale overdensity, contrary to the radial alignment of elliptical galaxy. Our results explain the origin of detected positive GI term from the weak-lensing surveys. We conclude that in future analysis, the GI model must include the dependence on galaxy types in more detail.Comment: 23 pages, 13 figures, published in ApJ. Our mock galaxy catalog is available upon request by email to the author ([email protected], [email protected]