Energy Spectrum Extraction and Optimal Imaging via Dual-Energy Material Decomposition

Inferior soft-tissue contrast resolution is a major limitation of current CT scanners. The aim of the study is to improve the contrast resolution of CT scanners using dual-energy acquisition. Based on dual-energy material decomposition, the proposed method starts with extracting the outgoing energy spectrum by polychromatic forward projecting the material-selective images. The extracted spectrum is then reweighted to boost the soft-tissue contrast. A simulated water cylinder phantom with inserts that contain a series of six solutions of varying iodine concentration (range, 0-20 mg/mL) is used to evaluate the proposed method. Results show the root mean square error (RMSE) and mean energy difference between the extracted energy spectrum and the spectrum acquired using an energy-resolved photon counting detector(PCD), are 0.044 and 0.01 keV, respectively. Compared to the method using the standard energy-integrating detectors, dose normalized contrast-to-noise ratio (CNRD) for the proposed method are improved from 1 to 2.15 and from 1 to 1.88 for the 8 mg/mL and 16 mg/mL iodine concentration inserts, respectively. The results show CT image reconstructed using the proposed method is superior to the image reconstructed using the standard method that using an energy-integrating detector.Comment: 4 pages, 4 figures in The 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference Recor

Multi-View 3D Object Detection Network for Autonomous Driving

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Jatrorrhizine inhibits liver cancer cell growth by targeting the expressions of miR-221-3p and miR-15b-5p

Purpose: To investigate the effect of jatrorrhizine on hepatic cancer cell proliferation and its mechanism of action. Methods: Jatrorrhizine-mediated changes in cell viability were measured using (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, while apoptosis induction was evaluated by flow cytometry. Transwell assay was used for the measurement of cell invasion, whereas cell migration was assessed by wound healing assay. The protein expression of Axin2 was determined with western blotting assay. Results: Jatrorrhizine significantly (p < 0.049) suppressed the viability of HepG2 and HCCLM3 cells in the concentration range of 0.5 to 16.0 μM. Treatment of HepG2 and HCCLM3 cells with 4.0 μM jatrorrhizine markedly suppressed cell invasion, when compared to untreated cells (p < 0.0493). Jatrorrhizine significantly promoted the apoptosis of HepG2 and HCCLM3 cells at 48 h, relative to untreated cells, but 16.0 μM jatrorrhizine markedly suppressed the expressions of miR-221-3p and miR-15b-5p (p < 0.0493). Moreover, jatrorrhizine significantly up-regulated the protein expressions of Axin2 in HepG2 and HCCLM3 cells at 48 h (p < 0.0493). Conclusion: Jatrorrhizine inhibits the proliferation, and suppressed the invasiveness and migration of HepG2 and HCCLM3 liver cancer cells, but increases their apoptosis. Moreover, it down-regulates the expressions of miR-221-3p and miR15b-5p and promotes Axin2 protein expression in HepG2 and HCCLM3 cells. Therefore, jatrorrhizine is a potential drug candidate for the treatment of liver cancer

Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels

In this paper, we investigate the task of zero-shot human-object interaction (HOI) detection, a novel paradigm for identifying HOIs without the need for task-specific annotations. To address this challenging task, we employ CLIP, a large-scale pre-trained vision-language model (VLM), for knowledge distillation on multiple levels. Specifically, we design a multi-branch neural network that leverages CLIP for learning HOI representations at various levels, including global images, local union regions encompassing human-object pairs, and individual instances of humans or objects. To train our model, CLIP is utilized to generate HOI scores for both global images and local union regions that serve as supervision signals. The extensive experiments demonstrate the effectiveness of our novel multi-level CLIP knowledge integration strategy. Notably, the model achieves strong performance, which is even comparable with some fully-supervised and weakly-supervised methods on the public HICO-DET benchmark