Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection

LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints. Though seemingly natural, how to efficiently combine them for improved feature representation is still unclear. The main challenge arises from that Radar data are extremely sparse and lack height information. Therefore, directly integrating Radar features into LiDAR-centric detection networks is not optimal. In this work, we introduce a bi-directional LiDAR-Radar fusion framework, termed Bi-LRFusion, to tackle the challenges and improve 3D detection for dynamic objects. Technically, Bi-LRFusion involves two steps: first, it enriches Radar's local features by learning important details from the LiDAR branch to alleviate the problems caused by the absence of height information and extreme sparsity; second, it combines LiDAR features with the enhanced Radar features in a unified bird's-eye-view representation. We conduct extensive experiments on nuScenes and ORR datasets, and show that our Bi-LRFusion achieves state-of-the-art performance for detecting dynamic objects. Notably, Radar data in these two datasets have different formats, which demonstrates the generalizability of our method. Codes are available at https://github.com/JessieW0806/BiLRFusion.Comment: accepted by CVPR202

Lactose intolerance in adults: biological mechanism and dietary management

Lactose intolerance related to primary or secondary lactase deficiency is characterized by abdominal pain and distension, borborygmi, flatus, and diarrhea induced by lactose in dairy products. The biological mechanism and lactose malabsorption is established and several investigations are available, including genetic, endoscopic and physiological tests. Lactose intolerance depends not only on the expression of lactase but also on the dose of lactose, intestinal flora, gastrointestinal motility, small intestinal bacterial overgrowth and sensitivity of the gastrointestinal tract to the generation of gas and other fermentation products of lactose digestion. Treatment of lactose intolerance can include lactose-reduced diet and enzyme replacement. This is effective if symptoms are only related to dairy products; however, lactose intolerance can be part of a wider intolerance to variably absorbed, fermentable oligo-, di-, monosaccharides and polyols (FODMAPs). This is present in at least half of patients with irritable bowel syndrome (IBS) and this group requires not only restriction of lactose intake but also a low FODMAP diet to improve gastrointestinal complaints. The long-term effects of a dairy-free, low FODMAPs diet on nutritional health and the fecal microbiome are not well defined. This review summarizes recent advances in our understanding of the genetic basis, biological mechanism, diagnosis and dietary management of lactose intolerance

Small Intestinal Bacterial Overgrowth in Patients with Irritable Bowel Syndrome: Clinical Characteristics, Psychological Factors, and Peripheral Cytokines

Small intestinal bacterial overgrowth (SIBO) has been implicated in the pathogenesis of irritable bowel syndrome (IBS). Psychosocial factors and low-grade colonic mucosal immune activation have been suggested to play important roles in the pathophysiology of IBS. In total, 94 patients with IBS and 13 healthy volunteers underwent a 10 g lactulose hydrogen breath test (HBT) with concurrent Tc99m scintigraphy. All participants also completed a face-to-face questionnaire survey, including the Hospital Anxiety and Depression Scale, Life Event Stress (LES), and general information. Serum tumour necrosis factor-α, interleukin- (IL-) 6, IL-8, and IL-10 levels were measured. The 89 enrolled patients with IBS and 13 healthy controls had no differences in baseline characteristics. The prevalence of SIBO in patients with IBS was higher than that in healthy controls (39% versus 8%, resp.; p=0.026). Patients with IBS had higher anxiety, depression, and LES scores, but anxiety, depression, and LES scores were similar between the SIBO-positive and SIBO-negative groups. Psychological disorders were not associated with SIBO in patients with IBS. The serum IL-10 level was significantly lower in SIBO-positive than SIBO-negative patients with IBS

Diagnostic Ability of Magnifying Narrow-Band Imaging for the Extent of Early Gastric Cancer: A Systematic Review and Meta-Analysis

Background. Accurate delineation of tumor margin is essential for complete resection of early gastric cancer (EGC). The objective of this study is to assess the performance of magnifying endoscopy with narrow-band imaging (ME-NBI) for the accurate demarcation of EGC margins. Methods. We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases up to March 2020 to identify eligible studies. The diagnostic accuracy of ME-NBI for EGC margins was calculated, and subgroup analyses were performed based on tumor size, depth of tumor invasion, tumor-occupied site, macroscopic type, histological type, Helicobacter pylori (H. pylori), and endoscopists’ experience. Besides, we also evaluated the negative and positive resection rates of the horizontal margin (HM) of EGC after endoscopic submucosal dissection (ESD) and surgery. Results. Ten studies comprising 1018 lesions were eligible in the databases. The diagnostic accuracy of ME-NBI for the demarcation of EGC margins was 92.4% (95% confidence interval (CI): 86.7%-96.8%). According to ME-NBI subgroup analyses, the rate of accurate evaluation of EGC margins was not associated with H. pylori infection status, tumor size, depth of tumor invasion, tumor-occupied site, macroscopic type, histological type, and endoscopists’ experience, and no statistical differences were found in subgroup analyses. Moreover, the negative and positive resection rates of HM after ESD and surgery were 97.4% (95% CI: 92.1%-100%) and 2.6% (95% CI: 0.02%-7.9%), respectively. Conclusions. ME-NBI enables a reliable delineation of the extent of EGC

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

Recent advances on 3D object detection heavily rely on how the 3D data are represented, \emph{i.e.}, voxel-based or point-based representation. Many existing high performance 3D detectors are point-based because this structure can better retain precise point positions. Nevertheless, point-level features lead to high computation overheads due to unordered storage. In contrast, the voxel-based structure is better suited for feature extraction but often yields lower accuracy because the input data are divided into grids. In this paper, we take a slightly different viewpoint -- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy. Bearing this view in mind, we devise a simple but effective voxel-based framework, named Voxel R-CNN. By taking full advantage of voxel features in a two stage approach, our method achieves comparable detection accuracy with state-of-the-art point-based models, but at a fraction of the computation cost. Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region Proposal Network and a detect head. A voxel RoI pooling is devised to extract RoI features directly from voxel features for further refinement. Extensive experiments are conducted on the widely used KITTI Dataset and the more recent Waymo Open Dataset. Our results show that compared to existing voxel-based methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a real-time frame processing rate, \emph{i.e}., at a speed of 25 FPS on an NVIDIA RTX 2080 Ti GPU. The code is available at \url{https://github.com/djiajunustc/Voxel-R-CNN}.Comment: AAAI202