检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Sun, Wei Min, Xiongkuo Tu, Danyang Zhai, Guangtao Ma, Siwei The Institute of Image Communication and Information Processing Shanghai Jiao Tong University China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

Image quality assessment (IQA) is very important for both end-users and service providers since a high-quality image can significantly improve the user’s quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code is available at https://***/sunwei925/StairIQA. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCai 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Learning Texture Transformer Network for Image Super-Resolution

Learning Texture Transformer Network for Image Super-Resolut...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fuzhi Yang Huan Yang Jianlong Fu Hongtao Lu Baining Guo Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Microsoft Research Beijing P.R. China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively. TTSR consists of four closely-related modules optimized for image generation tasks, including a learnable texture extractor by DNN, a relevance embedding module, a hard-attention module for texture transfer, and a soft-attention module for texture synthesis. Such a design encourages joint feature learning across LR and Ref images, in which deep feature correspondences can be discovered by attention, and thus accurate texture features can be transferred. The proposed texture transformer can be further stacked in a cross-scale way, which enables texture recovery from different levels (e.g., from 1x to 4x magnification). Extensive experiments show that TTSR achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.

关键词： Feature extraction Image resolution Task analysis Gallium nitride Machine learning Indexes computer vision

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Robust spoken language understanding with RL-based value error recovery

arXiv

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arXiv 2020年

作者： Liu, Chen Zhu, Su Chen, Lu Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Spoken Language Understanding (SLU) aims to extract structured semantic representations (e.g., slot-value pairs) from speech recognized texts, which suffers from errors of Automatic Speech Recognition (ASR). To alleviate the problem caused by ASR-errors, previous works may apply input adaptations to the speech recognized texts, or correct ASR errors in predicted values by searching the most similar candidates in pronunciation. However, these two methods are applied separately and indep.ndently. In this work, we propose a new robust SLU framework to guide the SLU input adaptation with a rule-based value error recovery module. The framework consists of a slot tagging model and a rule-based value error recovery module. We pursue on an adapted slot tagging model which can extract potential slot-value pairs mentioned in ASR hypotheses and is suitable for the existing value error recovery module. After the value error recovery, we can achieve a supervision signal (reward) by comparing refined slot-value pairs with annotations. Since operations of the value error recovery are non-differentiable, we exploit policy gradient based Reinforcement Learning (RL) to optimize the SLU model. Extensive experiments on the public CATSLU dataset show the effectiveness of our proposed approach, which can improve the robustness of SLU and outperform the baselines by significant margins. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

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Multi-label Classification with High-rank and High-order label Correlations

arXiv

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to dep.ct the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Grand Challenge on Respiratory Sound Classification for SPRS...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Chen, Changyan Lin, Jilei Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Liu, Lu Ma, Hui Xie, Dan Wu, Lihua Yang, Haibo Yuan, Shuhua Chen, Mengjun Zhang, Bingxue Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China Shanghai Children's Medical Center School of Medicine Shanghai China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal and Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Fujian Children's Hospital China The First Affiliated Hospital of Henan University of Traditional Chinese Medicine China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (纸本)9798350300260

Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier. New testing set is prepared to evaluate the generalization performance of the submissions. Top 3 teams will present their work at IEEE BioCAS 2023 conference. © 2023 IEEE.

关键词： and machine-learning models data augmentation deep-learning models feature extractions grand challenge Open-source respiratory sound classification

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Cross-lingual universal dep.ndency parsing only from one monolingual treebank

arXiv

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arXiv 2020年

作者： Sun, Kailai Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Syntactic parsing is a highly linguistic processing task whose parser requires training on treebanks from the expensive human annotation. As it is unlikely to obtain a treebank for every human language, in this work, we propose an effective cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available. To reach satisfactory parsing accuracy among quite different languages, we introduce two language modeling tasks into dep.ndency parsing as multi-tasking. Assuming only unlabeled data from target languages plus the source treebank can be exploited together, we adopt a self-training strategy for further performance improvement in terms of our multi-task framework. Our proposed cross-lingual parsers are implemented for English, Chinese, and 22 UD treebanks. The empirical study shows that our cross-lingual parsers yield promising results for all target languages, for the first time, approaching the parser performance which is trained in its own target treebank. Copyright © 2020, The Authors. All rights reserved.

关键词： Modeling languages

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Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Grand Challenge on Respiratory Sound Classification for SPRS...

引用

2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Ma, Hui Liu, Lu Yuan, Shuhua Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

It is important to continuously monitor our respiratory system to prevent us from suffering respiratory-releated diseases. This demands for an automatic respiratory sounds software to speed up diagnosis and to reduce the workload of physicians. In the IEEE BioCAS 2022 conference, we have organized the first grand challenge on respiratory sound classification using the paediatric respiratory sound (SPRSound). This event has invited 45 teams with more than 100 open source entries and the top 5 teams are invited to present their works in the IEEE BioCAS 2022. © 2022 IEEE.

关键词： Open source software

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SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

arXiv

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arXiv 2023年

作者： Luo, Xiangde Fu, Jia Zhong, Yunxin Liu, Shuolin Han, Bing Astaraki, Mehdi Bendazzoli, Simone Toma-Dasu, Iuliana Ye, Yiwen Chen, Ziyang Xia, Yong Su, Yanzhou Ye, Jin He, Junjun Xing, Zhaohu Wang, Hongqiu Zhu, Lei Yang, Kaixiang Fang, Xin Wang, Zhiwei Lee, Chan Woong Park, Sang Joon Chun, Jaehee Ulrich, Constantin Maier-Hein, Klaus H. Ndipenoch, Nchongmaje Miron, Alina Li, Yongmin Zhang, Yimeng Chen, Yu Bai, Lu Huang, Jinlong An, Chengyang Wang, Lisheng Huang, Kaiwen Gu, Yunqi Zhou, Tao Zhou, Mu Zhang, Shichuan Liao, Wenjun Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Chengdu China Shanghai AI Lab Shanghai China Co. Ltd. Beijing China Department of Medical Radiation Physics Stockholm University Solna Sweden Department of Biomedical Engineering and Health Systems KTH Huddinge Sweden Northwestern Polytechnical University Xi’an China Guangzhou China Wuhan National Laboratory for Optoelectronics with MoE Key Laboratory for Biomedical Photonics China Yonsei University College of Medicine Seoul Korea Republic of Oncosoft Inc. Seoul Korea Republic of Heidelberg Germany Department of Computer Science Brunel University London Uxbridge United Kingdom MedMind Technology Co. Ltd. Beijing China Shanghai Jiao Tong University Shanghai China Nanjing University of Science and Technology Nanjing China

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCai2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge’s goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68% to 86.70%, and 70.42% to 73.44% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://***. © 2023, CC BY-NC-ND.

关键词： Radiotherapy

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End-to-end far-field speech recognition with unified dereverberation and beamforming

arXiv

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arXiv 2020年

作者： Zhang, Wangyou Subramanian, Aswin Shanmugam Chang, Xuankai Watanabe, Shinji Qian, Yanmin MoE Key Lab of Artificial Intelligence & SpeechLab Department of Computer Science and Engineering AI Institute Shanghai Jiao Tong University Shanghai China Center for Language and Speech Processing Johns Hopkins University United States

Despite successful applications of end-to-end approaches in multi-channel speech recognition, the performance still degrades severely when the speech is corrupted by reverberation. In this paper, we integrate the dereverberation module into the end-to-end multi-channel speech recognition system and explore two different frontend architectures. First, a multi-source mask-based weighted prediction error (WPE) module is incorporated in the frontend for dereverberation. Second, another novel frontend architecture is proposed, which extends the weighted power minimization distortionless response (WPD) convolutional beamformer to perform simultaneous separation and dereverberation. We derive a new formulation from the original WPD, which can handle multi-source input, and replace eigenvalue decomposition with the matrix inverse operation to make the back-propagation algorithm more stable. The above two architectures are optimized in a fully end-to-end manner, only using the speech recognition criterion. Experiments on both spatialized wsj1-2mix corpus and REVERB show that our proposed model outperformed the conventional methods in reverberant scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Beamforming

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