220 research outputs found
Galaxy classification with deep convolutional neural networks
No full textGalaxy classification, using digital images captured from sky surveys to determine the galaxy morphological classes, is of great interest to astronomy researchers. Conventional methods rely heavily on a few handcrafted morphological features while popular feature extraction methods that developed for natural images are not suitable for galaxy images. Deep convolutional neural networks (CNNs) are able to learn powerful features from images by hierarchical convolutional and pooling operations. This work applies state-of-the-art deep CNN technologies to galaxy classification for both a regression task and multi-class classification tasks. We also implement and compare the performance with several different conventional machine learning algorithms for a classification sub-task. Our experiments show that convolutional neural networks are able to learn representative features automatically and achieve high performance, surpassing both human recognition and other machine learning methods.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Honghui Shi, accepted the attached license on 2016-04-20 at 15:32.The student, Honghui Shi, submitted this Thesis for approval on 2016-04-20 at 15:46.This Thesis was approved for publication on 2016-04-26 at 09:28.DSpace SAF Submission Ingestion Package generated from Vireo submission #9386 on 2016-07-07 at 14:17:35Made available in DSpace on 2016-07-07T21:17:52Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-04-26Embargo set by: Seth Robbins for item 93294
Lift date: 2018-07-07T21:18:16Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 93294 on 2018-07-08T09:15:20Z
Deep learning in sequential data analysis
No full textDeep learning has achieved great success in recent years in computer vision and its related areas. For core computer vision tasks such as image classification, image semantic segmentation, image super-resolution, and object detection from images, deep learning based methods outperform various traditional methods in terms of both accuracy and speed. While a myriad of deep learning based computer vision research projects are continuously pushing forward the frontier of computer vision further by improving the performance for image-level tasks, many recent investigations have begun to look into deep learning based methods for sequential data such as videos and medical image sequences.
With the extra information from its additional sequential dimension, sequential data naturally raises an important and challenging question: How can we effectively and efficiently integrate such sequential information into existing successful and sophisticated image-based deep learning frameworks without building from scratch?
In this dissertation we develop techniques and methods that enable us to incorporate sequential information into existing image-based deep learning frameworks for different computer vision tasks. More specifically, we propose advanced methods that successfully utilize both image-based deep learning models and sequential information for the super-resolution task using multi-slice computed tomography image sequences, and for the object detection and tracking task using multi-frame videos. We demonstrate how we integrate sequential information into modern image-based deep learning systems for these different tasks under different integration paradigms. Our experiments show that our proposed methods have significantly improved the performances compared with naive image-based methods, and achieved the new state-of-the-art for such sequential vision tasks.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-12-01The student, Honghui Shi, accepted the attached license on 2017-12-05 at 09:43.The student, Honghui Shi, submitted this Dissertation for approval on 2017-12-05 at 09:48.This Dissertation was approved for publication on 2017-12-05 at 11:50.DSpace SAF Submission Ingestion Package generated from Vireo submission #11851 on 2018-03-13 at 10:37:39Made available in DSpace on 2018-03-13T17:35:49Z (GMT). No. of bitstreams: 2
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Previous issue date: 2017-12-05Embargo set by: Seth Robbins for item 105482
Lift date: 2020-03-13T17:36:05Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 105482 on 2020-03-14T09:15:28Z
Depth aware RCNN
No full textImage object detection networks that depend on region proposal networks (RPN) have achieved state-of-art results. As RPN is trained to share convolutional features with the actual classification layers in the network, features learned by the convolutional backbones may have subtle impact on the RPN. A successful approach comes from RGB-D image object detection, where the convolutional layers learn not just RGB features, but also depth features.
In this thesis, we study the problem of simultaneously localizing objects as well as estimating their depth. We propose to use one backbone network for two tasks and show that multi-task learning with shared weights can have reciprocating benefits. Our experiments show that when combined with depth prediction in the network, the object detection branch in our model outperforms Faster-RCNN on the challenging KITTI detection benchmark and the Cityscapes dataset. Likewise, the performance of our depth prediction branch is slightly better compared with methods using the same depth prediction architecture.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Tianxi Zhao, accepted the attached license on 2019-04-24 at 08:05.The student, Tianxi Zhao, submitted this Thesis for approval on 2019-04-24 at 08:06.This Thesis was approved for publication on 2019-04-24 at 09:28.DSpace SAF Submission Ingestion Package generated from Vireo submission #13865 on 2019-08-22 at 15:07:58Made available in DSpace on 2019-08-23T20:36:08Z (GMT). No. of bitstreams: 2
ZHAO-THESIS-2019.pdf: 12152227 bytes, checksum: 8f632c204c224a70a8ada07d01502b8f (MD5)
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Previous issue date: 2019-04-24Embargo set by: Seth Robbins for item 112201
Lift date: 2021-08-23T20:36:18Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112201 on 2021-08-24T09:15:31Z
Multiple scale sharing faster-RCNN
No full textThe student, Siwei Tang, submitted this Thesis for approval on 2019-04-24 at 10:10.This Thesis was approved for publication on 2019-04-24 at 13:17.DSpace SAF Submission Ingestion Package generated from Vireo submission #13771 on 2019-08-22 at 16:23:20Made available in DSpace on 2019-08-23T20:48:20Z (GMT). No. of bitstreams: 2
TANG-THESIS-2019.pdf: 681796 bytes, checksum: 2c015d2dfaaa358501f9c6f1ab80b395 (MD5)
LICENSE.txt: 4207 bytes, checksum: 59f71a91dfa4f8be3bed256450de5c74 (MD5)
Previous issue date: 2019-04-24Embargo set by: Seth Robbins for item 112352
Lift date: 2021-08-23T20:48:32Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 112352 on 2021-08-24T09:15:10Z.Small object detection is a challenging task in the field of computer vision because the objects are always of low resolution in the original image and can be easily affected by noise. The state-of-the-art Faster RCNN object detector has good capacity of detecting large objects while small object detection is not one of its advantages. This thesis presents a novel object detector Multi-Scale Sharing Faster-RCNN (MSS-FRCNN) to solve the problem of poor detection performance of small objects by Faster RCNN. We find that upsampling the input image can benefit the small object detection performance. So MSS-FRCNN takes two images with different scales as input and then uses the two feature maps extracted from two images for RoI generation independently. Finally, the model merges the two feature map for classification and bounding box regression. We test our model with two datasets Tsinghua-Tencent 100k and Pascal VOC 07+12. The result demonstrates that MSS-FRCNN can outperform original Faster RCNN in small object detection.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01The student, Siwei Tang, accepted the attached license on 2019-04-24 at 09:11
Nanotechnology-Enhanced Pharmacotherapy for Intervertebral Disc Degeneration Treatment
No full textShaoyan Shi, Xuehai Ou, Chao Liu, Rui Li, Qianjin Zheng, Leiming Hu Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of ChinaCorrespondence: Leiming Hu, Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of China, Email [email protected]: Intervertebral disc degeneration (IDD) is a primary contributor to chronic back pain and disability globally, with current therapeutic approaches often proving inadequate due to the complex nature of its pathophysiology. This review assesses the potential of nanoparticle-driven pharmacotherapies to address the intricate challenges presented by IDD. We initially analyze the primary mechanisms driving IDD, with particular emphasis on mitochondrial dysfunction, oxidative stress, and the inflammatory microenvironment, all of which play pivotal roles in disc degeneration. Then, we evaluate the application of metal-phenolic and catalytic nanodots in targeting mitochondrial defects and alleviating oxidative stress within the degenerative disc environment. Additionally, multifunctional and stimuli-responsive nanoparticles are explored for their capacity to provide precise targeting and controlled therapeutic release, offering improved localization and sustained delivery. Finally, we outline future research directions and identify emerging trends in nanoparticle-based therapies, highlighting their potential to significantly advance IDD treatment by overcoming the limitations of conventional therapeutic modalities and enabling more effective, targeted management strategies.Keywords: nanotechnology, drug delivery, intervertebral disc degeneration, regeneration, therap
Macrophage-Derived Extracellular Vesicles: A Novel Therapeutic Alternative for Diabetic Wound
No full textShaoyan Shi, Xuehai Ou, Qian Wang, Li Zhang Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of ChinaCorrespondence: Li Zhang, Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of China, Email [email protected]: Diabetic wounds represent a significant clinical and economic challenge owing to their chronicity and susceptibility to complications. Dysregulated macrophage function is a key factor in delayed wound healing. Recent studies have emphasized the therapeutic potential of macrophage-derived extracellular vesicles (MDEVs), which are enriched with bioactive molecules such as proteins, lipids, and nucleic acids that mirror the state of their parent cells. MDEVs influence immune modulation, angiogenesis, extracellular matrix remodeling, and intercellular communication. In this review, we summarize and discuss the biological properties and therapeutic mechanisms of MDEVs in diabetic wound healing, highlighting strategies to enhance their efficacy through bioengineering and advanced delivery systems. We also explore the integration of MDEVs into innovative wound care technologies. Addressing current limitations and advancing clinical translation of MDEVs could advance diabetic wound management, offering a precise, effective, and versatile therapeutic option.Keywords: extracellular vesicle, diabetic wound, macrophage, nanomedicine, therap
Multiple scale sharing faster-RCNN
No full textSmall object detection is a challenging task in the field of computer vision because the objects are always of low resolution in the original image and can be easily affected by noise. The state-of-the-art Faster RCNN object detector has good capacity of detecting large objects while small object detection is not one of its advantages. This thesis presents a novel object detector Multi-Scale Sharing Faster-RCNN (MSS-FRCNN) to solve the problem of poor detection performance of small objects by Faster RCNN. We find that upsampling the input image can benefit the small object detection performance. So MSS-FRCNN takes two images with different scales as input and then uses the two feature maps extracted from two images for RoI generation independently. Finally, the model merges the two feature map for classification and bounding box regression. We test our model with two datasets Tsinghua-Tencent 100k and Pascal VOC 07+12. The result demonstrates that MSS-FRCNN can outperform original Faster RCNN in small object detection.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste
Nanoparticle-Based Therapeutics for Enhanced Burn Wound Healing: A Comprehensive Review
No full textShaoyan Shi, Xuehai Ou, Jiafeng Long, Xiqin Lu, Siqi Xu, Li Zhang Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of ChinaCorrespondence: Li Zhang, Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an Honghui Hospital North District, Xi’an, Shaanxi, 710000, People’s Republic of China, Tel/ Fax +86 029-83661911, Email [email protected]: Burn wounds pose intricate clinical challenges due to their severity and high risk of complications, demanding advanced therapeutic strategies beyond conventional treatments. This review discusses the application of nanoparticle-based therapies for optimizing burn wound healing. We explore the critical phases of burn wound healing, including inflammation, proliferation, and remodeling, while summarizing key nanoparticle-based strategies that influence these processes to optimize healing. Various nanoparticles, such as metal-based, polymer-based, and extracellular vesicles, are evaluated for their distinctive properties and mechanisms of action, including antimicrobial, anti-inflammatory, and regenerative effects. Future directions are highlighted, focusing on personalized therapies and the integration of sophisticated drug delivery systems, emphasizing the transformative potential of nanoparticles in enhancing burn wound treatment.Keywords: nanomedicine, burns, wound, regeneration, therap
Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation
No full textWe consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. Based on the observation that stuff categories usually share similar appearances across images of different domains while things (i.e. object instances) have much larger differences, we propose to improve the semantic-level alignment with different strategies for stuff regions and for things: (1) for the stuff categories, we generate the feature representation for each class and conduct the alignment operation from the target domain to the source domain; (2) for the thing categories, we generate the feature representation for each individual instance and encourage the instance in the target domain to align with the most similar one in the source domain. In this way, the individual differences within thing categories will also be considered to alleviate over-alignment. In addition to our proposed method, we further reveal the reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy and show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains. We conduct extensive experiments in two unsupervised domain adaptation tasks, GTA5 to Cityscapes and SYNTHIA to Cityscapes, and achieve the new state-of-the-art segmentation accuracy.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01The student, Zhonghao Wang, accepted the attached license on 2019-11-22 at 14:13.The student, Zhonghao Wang, submitted this Thesis for approval on 2019-11-22 at 14:25.This Thesis was approved for publication on 2019-11-22 at 15:53.DSpace SAF Submission Ingestion Package generated from Vireo submission #14591 on 2020-02-28 at 17:36:44Made available in DSpace on 2020-03-02T22:38:46Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-11-22Embargo set by: Seth Robbins for item 114001
Lift date: 2022-03-02T22:39:04Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 114001 on 2022-03-03T10:15:27Z
Emerging Nanotherapeutic Approaches for Diabetic Wound Healing
No full textShaoyan Shi, Leiming Hu, Dong Hu, Xuehai Ou, Yansheng Huang Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an, 710000, People’s Republic of ChinaCorrespondence: Yansheng Huang, Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an, 710000, People’s Republic of China, Email [email protected]: Diabetic wounds pose a significant challenge in modern healthcare due to their chronic and complex nature, often resulting in delayed healing, infections, and, in severe cases, amputations. In recent years, nanotherapeutic approaches have emerged as promising strategies to address the unique pathophysiological characteristics of diabetic wounds. This review paper provides a comprehensive overview of the latest advancements in nanotherapeutics for diabetic wound treatment. We discuss various nanomaterials and delivery systems employed in these emerging therapies. Furthermore, we explore the integration of biomaterials to enhance the efficacy of nanotherapeutic interventions. By examining the current state-of-the-art research, challenges, and prospects, this review aims to offer valuable insights for researchers, clinicians, and healthcare professionals working in the field of diabetic wound care.Keywords: diabetic wounds, nanoparticle, biomaterials, DELIVERY syste
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