Training with confident positive-labeled instances has received a lot of attention in Positive and Unlabeled(PU)learning tasks,and this is formally termed“Instance-Dependent PU learning”.In instance-dependent PU lea...Training with confident positive-labeled instances has received a lot of attention in Positive and Unlabeled(PU)learning tasks,and this is formally termed“Instance-Dependent PU learning”.In instance-dependent PU learning,whether a positive instance is labeled depends on its labeling confidence.In other words,it is assumed that not all positive instances have the same probability to be included by the positive set.Instead,the instances that are far from the potential decision boundary are with larger probability to be labeled than those that are close to the decision boundary.This setting has practical importance in many real-world applications such as medical diagnosis,outlier detection,object detection,etc.In this survey,we first present the preliminary knowledge of PU learning,and then review the representative instance-dependent PU learning settings and methods.After that,we thoroughly compare them with typical PU learning methods on various benchmark datasets and analyze their performances.Finally,we discuss the potential directions for future research.展开更多
Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical practice.However,due to scale variation and blurry polyp boundaries,it is still a challenging task to ach...Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical practice.However,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes.In this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation.Specifically,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer strategy.Besides,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale variation.Further,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual information.Finally,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation maps.Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Our implementation code and segmentation maps will be publicly at https://github.com/taozh2017/EFANet.展开更多
Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of humans.Medical AI algorithms have been developed for disease diagnosis and prediction and t...Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of humans.Medical AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past decade.Color fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in ophthalmology.In September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.展开更多
Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past decade.Since light fields record comprehensive information of natural scenes that benefit SOD in a...Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past decade.Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend.This paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency community.Firstly,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief review.Existing datasets for light field SOD are also summarized.Secondly,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD models.Due to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and uniform.Our supplemental data make a universal benchmark possible.Lastly,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection tasks.We provide nine observations on challenges and future directions,and outline several open issues.All the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://github.com/kerenfu/LFSOD-Survey.展开更多
基金supported by National Natural Science Foundation of China(62336003,12371510)NSF for Distinguished Young Scholar of Jiangsu Province(BK20220080).
文摘Training with confident positive-labeled instances has received a lot of attention in Positive and Unlabeled(PU)learning tasks,and this is formally termed“Instance-Dependent PU learning”.In instance-dependent PU learning,whether a positive instance is labeled depends on its labeling confidence.In other words,it is assumed that not all positive instances have the same probability to be included by the positive set.Instead,the instances that are far from the potential decision boundary are with larger probability to be labeled than those that are close to the decision boundary.This setting has practical importance in many real-world applications such as medical diagnosis,outlier detection,object detection,etc.In this survey,we first present the preliminary knowledge of PU learning,and then review the representative instance-dependent PU learning settings and methods.After that,we thoroughly compare them with typical PU learning methods on various benchmark datasets and analyze their performances.Finally,we discuss the potential directions for future research.
基金supported in part by National Natural Science Foundation of China(Nos.62172228,62201263,62106043 and 62201265).
文摘Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical practice.However,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes.In this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation.Specifically,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer strategy.Besides,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale variation.Further,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual information.Finally,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation maps.Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Our implementation code and segmentation maps will be publicly at https://github.com/taozh2017/EFANet.
基金This work was supported by the National Natural Science Foundation of China(82200961)the Science and Technology Commission of Shanghai(20DZ2270800)+1 种基金the Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology(2022SKLE-KFKT004)the China Postdoctoral Science Foundation(2022M720091,2023M741708,2023TQ0159,and GZC20233503)。
文摘Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of humans.Medical AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past decade.Color fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in ophthalmology.In September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.
基金supported by the National Natural Science Foundation of China(Nos.62176169 and 61703077)SCU-Luzhou Municipal People's Government Strategic Cooperation Projetc(t No.2020CDLZ-10)+1 种基金supported by the National Natural Science Foundation of China(No.62172228)supported by the National Natural Science Foundation of China(No.61773270).
文摘Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past decade.Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend.This paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency community.Firstly,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief review.Existing datasets for light field SOD are also summarized.Secondly,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD models.Due to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and uniform.Our supplemental data make a universal benchmark possible.Lastly,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection tasks.We provide nine observations on challenges and future directions,and outline several open issues.All the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://github.com/kerenfu/LFSOD-Survey.
