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An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

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Tsinghua Science and Technology 2020年第3期25卷 368-383页

作者： Guangyuan Zheng Guanghui Han Nouman Qadeer Soomro the Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of TechnologyBeijing 100081China the School of Information Technology Shangqiu Normal UniversityShangqiu 476000China the School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510006China the Department of Software Engineering Mehran University of Engineering and TechnologySZAB CampusKhairpur Mir's66020Pakistan

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network(CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion(AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training.

关键词： sign lung cancer pulmonary nodule Convolutional Neural Network(CNN) Artificial Immune Algorithm(AIA)

Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Lin, Qiuzhen Tan, Kay Chen Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China College of Computer Science and Software Engineering Shenzhen University China Department of Computing Hong Kong Polytechnic University Hong Kong

We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in VLSMOPs intensifies the curse of dimensionality and poses significant challenges for existing large-scale evolutionary multiobjective algorithms, rendering them more difficult to solve within the constraints of practical computing resources. To overcome this issue, we propose a novel approach called the very large-scale multiobjective optimization framework (VMOF). The method efficiently samples general yet suitable evolutionary directions in the very large-scale space and subsequently fine-tunes these directions to locate the Pareto-optimal solutions. To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations. Furthermore, a technique is designed for fine-tuning directions specific to tracking Pareto-optimal solutions. To understand the designed framework, we present our analysis of the framework and then evaluate VMOF using widely recognized benchmarks and real-world problems spanning dimensions from 100 to 1,000,000. Experimental results demonstrate that our method exhibits superior performance not only on LSMOPs but also on VLSMOPs when compared to existing algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

Selective Multi-Scale Learning for Object Detection

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arXiv 2022年

作者： Chen, Junliang Lu, Weizeng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP. © 2022, CC BY.

关键词： Object detection

Reset Controller Synthesis by Reach-avoid Analysis for Delay Hybrid Systems

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arXiv 2023年

作者： Su, Han Zhu, Jiyu Feng, Shenghua Bai, Yunjun Gu, Bin Liu, Jiang Yang, Mengfei Zhan, Naijun Institute of Software University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Beijing Institute of Control Engineering Beijing China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China China Academy of Space Technology Beijing China School of Computer Science Peking University Beijing China

A reset controller is pivotal in the design of hybrid systems. It restricts the initial set and redefines the reset map associated with discrete transitions, ensuring system objective is met. Reset controller synthesis, alongside feedback controller synthesis and switching logic controller synthesis, provides a correct-by-construction approach to designing hybrid systems. However, the presence of time-delay poses challenges in hybrid systems, potentially compromising control performance and rendering verification certificates obtained by abstracting away time-delay invalid in practice. This paper addresses this issue by proposing a approach that incorporates time-delay considerations. We introduce a method that reduces the synthesis of reset controllers to the generation of reach-avoid sets for the hybrid system at hand, which can be efficiently solved using standard convex optimization solvers. © 2023, CC BY-NC-ND.

关键词： Controllers

CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of Computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

Where to Start: Studying Type Annotation Practices in Python 21

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Where to Start: Studying Type Annotation Practices in Python

IEEE International Conference on Automated software engineering (ASE)

作者： Wuxia Jin Dinghong Zhong Zifan Ding Ming Fan Ting Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University

ISBN: (纸本)9781665447843

Dynamic programming languages have been embracing gradual typing, which supports optional type annotations in source code. Type-annotating a complex and long-lasting codebase is indeed a gradual and expensive process, where two issues have troubled developers. First, there is few guidance about how to implement type annotations due to the existence of non-trivial type practices; second, there is few guidance about which portion of a codebase should be type-annotated first. To address these issues, this paper investigates the patterns of non-trivial type-annotation practices and features of type-annotated code files. Our study detected six patterns of type-annotation practices, which involve recovering and expressing design concerns. Moreover, we revealed three complementary features of type-annotated files. Besides, we implemented a tool for studying optional typing practice. We suggest that: 1) design concerns should be considered to improve type annotation implementation by following at least six patterns; 2) files critical to software architecture could be type-annotated in priority. We believe these guidelines would promote a better type annotation practice for dynamic languages.

关键词： Codes Costs Annotations software architecture Maintenance engineering Feature extraction Dynamic programming

Xgboost Algorithm Based on Sensor Data Driven: Realizing In-Situ and On-Line Estimate of Field Capacity

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SSRN

SSRN 2023年

作者： Zhangzhong, Lili Kan, Xiaoqing Yu, Jingxin Zheng, Wengang Liu, Changbin Zhao, Chunjiang Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China National Engineering Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information Ministry of Agriculture Beijing100097 China

Field capacity (FC) is an important physical parameter to characterize soil properties, which is widely used in agricultural irrigation, hydrological simulation, and other fields. However, the current measurement methods are mainly laboratory tests with complex procedures. With the development of Internet of Things technology, exploring simple and intelligent online monitoring technology is possible. This research proposes a data-driven intelligent online measurement method of field capacity, which realizes the dynamic description of soil-water change curve at different depths by introducing the massive soil moisture data obtained by sensor technology to improve the frequency of field capacity capture. Moreover, an improved gradient lifting algorithm is proposed to realize field capacity intelligent recognition based on the learning of soil-water change curve characteristic. The feasibility and effectiveness of the method were verified using the data set of 118 farmland soil moisture monitoring points in the main grain producing areas of China. The duration of soil-water change curve at the monitoring point is 12.75-43.00 h, mainly from July to September, and the field capacity is between 23.48 and 38.83m3·m-3. The new monitoring method shows high accuracy and performance (R2 is within 0.93-0.95, RMSE is within 1.95%-2.16%), with significantly improved convenience. The intelligent monitoring and dynamic updating of field capacity can be realized only by installing soil moisture monitoring sensors. This method has prospects in practical applications, such as intelligent irrigation system based on soil monitoring and regional crop drought early warning system. © 2023, The Authors. All rights reserved.

关键词： Soil moisture

Efficient Last-iterate Convergence Algorithms in Solving Games

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arXiv 2023年

作者： Meng, Linjian Zhang, Youzhi Ge, Zhenxing Yang, Shangdong Ding, Tianyu Li, Wenbin Yang, Tianpei An, Bo Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications China Microsoft Corporation United States School of Computer Science and Engineering Nanyang Technological University Singapore

To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies reformulate learning an NE of the original EFG as learning the NEs of a sequence of (perturbed) regularized EFGs. Consequently, proving last-iterate convergence in solving the original EFG reduces to proving last-iterate convergence in solving (perturbed) regularized EFGs. However, the empirical convergence rates of the algorithms in these studies are suboptimal, since they do not utilize Regret Matching (RM)-based CFR algorithms to solve perturbed EFGs, which are known the exceptionally fast empirical convergence rates. Additionally, since solving multiple perturbed regularized EFGs is required, fine-tuning across all such games is infeasible, making parameter-free algorithms highly desirable. In this paper, we prove that CFR+, a classical parameter-free RM-based CFR algorithm, achieves last-iterate convergence in learning an NE of perturbed regularized EFGs. Leveraging CFR+ to solve perturbed regularized EFGs, we get Reward Transformation CFR+ (RTCFR+). Importantly, we extend prior work on the parameter-free property of CFR+, enhancing its stability, which is crucial for the empirical convergence of RTCFR+. Experiments show that RTCFR+ significantly outperforms existing algorithms with theoretical last-iterate convergence guarantees. Copyright © 2023, The Authors. All rights reserved.

关键词： Nash equilibrium