full-pixel semantic segmentation 1, p. 146, 2009. [Project] In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. Mar. eCollection 2022. 3, pp. The results demonstrated that the model can precisely detect safe and unsafe actions conducted by workers on-site. The IoU is the ratio of the intersection and the union of the default box to the ground truth box. 313, no. Cybern. The distance metric ASD refers to the ASD of segmentation result \(R\) and ground-truth result G. a The input of the system is a 3D CBCT scan. The Jaccard overlap is also called the IoU (Intersection over Union), or the Jaccard similarity coefficient. A top-down manner-based DCNN architecture for semantic image segmentation. Among the 3261 images, 2769 images were divided into the training set, 339 images were divided into the validation set, and 153 images were divided into the test set. 1c, where the individual teeth and surrounding bones are marked with different colors. There are problems such as a wide range of operations and difficult management of site workers. Gulshan, V. et al. [Paper] 49, 11231136 (2018). 2nd of the most influential papers in ICLR 2021, 7th of the most influential papers in ICLR 2020, 6th of the most influential papers in ICCV 2017, 3rd of the most influential papers in NIPS 2016, 4th International Workshop on official website and that any information you provide is encrypted The methods construct convolutional neural networks with different depths to detect safety helmets. In the course of training, the change of the mean average precision (mAP) and the loss function during training was recorded by TensorBoard. The layers are Input, hidden, pattern/summation and output. My current research focus is on deep learning for high-level vision. Jifeng Dai, Kaiming He, and Jian Sun The SSD algorithm defines the total loss function as the weighted sum between localization loss and confidence loss: In the prediction process, the object classes and confidence scores will be confirmed according to the maximum class confidence score and the prediction box that belongs to the background will be filtered out. 2020: Two papers are accepted by ECCV 2020. Head injuries are very serious and often fatal. Video SOD: Add four AAAI22 papers, one ACMM22 paper, two ECCV22 Adv. Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. Biol. Then, using PDF of each class, the class probability of a new input is This work was supported in part by National Natural Science Foundation of China (grant number 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006). [Project] Article MathSciNet It can solve the problems of too many parameters and difficult training of the deep neural networks and can get better classification effects. [Code], Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang, "Gated Fusion Network for Single Image Dehazing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. This study was financially supported by the National Key R&D Program of China (Grant no. 39, pp. We have validated our system in real-world clinical scenarios with very large internal (i.e., 1359 CBCT scans) and external (i.e., 407 CBCT scans) datasets, and obtained high accuracy and applicability as confirmed by various experiments. [Paper] Previous studies have demonstrated the effectiveness of locating the safety helmets and workers and detecting the helmets. Accurate and robust segmentation of CBCT images for these patients is essential in the workflow of digital dentistry. V-net: Fully convolutional neural networks for volumetric medical image segmentation. (2) The safety helmets of incomplete shapes and small sizes are hard to be recognized. Deformable DETR ranks 2nd of the most influential papers in ICLR 2021, VL-BERT ranks 7th of the most influential papers in ICLR 2020, Deformable ConvNets ranks 6th of the most influential papers in ICCV 2017, R-FCN ranks 3rd of the most influential papers in NIPS 2016. Jifeng Dai, Jianjiang Feng, and Jie Zhou Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. However, the detection model has a poor performance when the images are not very clear, the safety helmets are too small and obscure, and the background is too complex as shown in Figure 10. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Y., Hou, Y., Zhang, S., Shan, J., 2019. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. And the model does not require the selection of handcraft features and has a good capacity of extracting features in the images. Med. [11]). 4e. Therefore, in the paper, the SSD-MobileNet model is selected to detect safety helmets worn by the workers. & Wipf, D. Revisiting deep intrinsic image decompositions. J. Orthod. Individual tooth segmentation from CT images using level set method with shape and intensity prior. [2] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). Y., Hou, Y., Zhang, S., Shan, J., 2019. and D.S. Clipboard, Search History, and several other advanced features are temporarily unavailable. Unable to load your collection due to an error, Unable to load your delegates due to an error. In contrast, since the external dataset is collected from different dental clinics, the distribution of its dental abnormalities is a little different compared with the internal set. It should be used for academic research only. Keustermans, J., Vandermeulen, D. & Suetens, P. Integrating statistical shape models into a graph cut framework for tooth segmentation. [Project] If you are interested in internship, Ph.D. program, postdoctoral positions related to computer vision or deep learning, please send me an email. Video SOD: Add four AAAI22 papers, one ACMM22 paper, two ECCV22 Therefore, there are six default boxes of different sizes for each feature cell. a The overall intensity histogram distributions of the CBCT data collected from different manufacturers. [Code], Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, and Ming-Hsuan Yang, "Learning a Discriminative Prior for Blind Image Deblurring", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. There are 250 true positive objects, 12 false positive objects, and 73 false negative objects in the detected images. Also, the worker close to the camera failed to be recognized. Jan. 2022: One paper is accepted by IJCV and three papers are accepted by IEEE TIP. [Project] Han Hu+, Jiayuan Gu*+, Zheng Zhang+, Jifeng Dai, and Yichen Wei Med. VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide TP+FP is the number of helmets detected. In safety management at the construction site, it is essential to supervise the safety protective equipment wearing condition of the construction workers. To verify the clinical applicability of our AI system in more detail, we randomly selected 100 CBCT scans from the external set, and compared the segmentation results produced by our AI system and expert radiologists. AnatomyNet: Deep learning for fast and fully automated wholevolume segmentation of head and neck anatomy : Medical Physics: 2018: FCN: CT: Liver-Liver Tumor: Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields : MICCAI: 2016: 3D-CNN: MRI: Spine Up-convolutional architectures like the fully convolutional networks for semantic segmentation and the u-net are still not wide spread and we know of only one attempt to generalize such an architecture to 3D . IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. Schematic illustration of MobileNet separate convolution. 86, pp. Therefore, the SSD algorithm uses the hard negative mining strategy to avoid the significant imbalance between the positive and negative training examples. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. For example, instead of simply localizing each tooth by points or bounding boxes as used in these competing methods, our AI system learns a hierarchical morphological representation (e.g., tooth skeleton, tooth boundary, and root apices) for individual teeth often with varying shapes, and thus can more effectively characterize each tooth even with blurring boundaries using small training dataset. Khalid, A. M. International designation system for teeth and areas of the oral cavity. IEEE Access 8, 9729697309 (2020). & Laio, A. Clustering by fast search and find of density peaks. 3, pp. All the images that contained safety helmets were manually prelabeled, using the open-source tool LabelImage (available in https://github.com/tzutalin/labelImg). The research is limited by the restricted activities working at heights and the dataset size. Second, for all methods (including our AI system), the data argumentation techniques (100+) can consistently improve the segmentation accuracy. Multi-feature Based High-resolution Palmprint Recognition Automatic medical image segmentation plays a critical role in scientific research and medical care. Chen, Y. et al. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized Weijie Su*+, Xizhou Zhu*+, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai I got my Ph.D. degree from the Department of Automation, Tsinghua University in 2014, under the supervison of Professor Jie Zhou. 1a, we can find that there are large appearance variations across data, indicating necessity of collecting a large-scale dataset for developing an AI system with good robustness and generalizability. In this sense, an adequate ratio of 8:1:1 according to the previous experience is adopted in our study. IEEE Trans Pattern Anal Mach Intell. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Moreover, the multi-task learning scheme with boundary prediction can greatly reduce the ASD error, especially on the CBCT images with blurry boundaries (e.g., with metal artifacts). Apr. In this study, we develop a deep-learning-based AI system that is clinically stable and accurate for fully automatic tooth and alveolar bone segmentation from dental CBCT images.