2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第10页

Weakly Supervised Anomaly Localization and Segmentation of Biomarkers in OCT Images OCT图像中生物标记物的弱监督异常定位与分割

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00159

Xiaoming Liu, Qi Liu

{"title":"Weakly Supervised Anomaly Localization and Segmentation of Biomarkers in OCT Images","authors":"Xiaoming Liu, Qi Liu","doi":"10.1109/ICTAI56018.2022.00159","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00159","url":null,"abstract":"Identifying biomarkers from optical coherence tomography images is critical in diagnosing and treating ophthalmic diseases. Most existing biomarker segmentation methods require pixel-level annotations for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised biomarker localization and segmentation method. The framework includes a classification network and a teacher-student network to exploit category annotated data through contrastive learning and anomaly localization strategies based on knowledge distillation. The classification network combines cross-entropy loss and self- supervised contrastive loss to ensure that the model focuses on the characteristics of the biomarker of interest. We introduce a knowledge distillation-based anomaly localization method to localize biomarker-related pathological regions accurately. The trained classification network acts as a teacher model to guide the training of the student network to learn the features of normal OCT images. The biomarker regions can be accurately localized by the differences between the feature maps generated by the two networks. Experiment results on the public dataset demonstrate the effectiveness of the proposed method.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Drawing Psychoanalysis via House-Tree-Person Test 通过房子-树-人测试自动绘图心理分析

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00171

Ting Pan, Xiaoming Zhao, Baodi Liu, Weifeng Liu

{"title":"Automated Drawing Psychoanalysis via House-Tree-Person Test","authors":"Ting Pan, Xiaoming Zhao, Baodi Liu, Weifeng Liu","doi":"10.1109/ICTAI56018.2022.00171","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00171","url":null,"abstract":"The increase of human psychological illness in today's fast paced and high stress world makes it essential to detect the warning signals of psychological problems. As the most representative drawing psychoanalysis method, House-Tree-Person (HTP) test is widely used in psychological assessment with the benefit of simplicity, non-verbal, and repeatability. HTP test can reveal the individual subconscious of the psychological state through the picture content of house, tree, and person drawn by the patient. Currently, HTP test is conducted by the therapist in person, which makes it time consuming and the results are mostly affected by the therapist's experience. Therefore, it is helpful and necessary to build an automated method to improve the objectivity, reliability, and efficiency of HTP test. In this paper, we propose an automated psychometric drawing screening method that forms the relationship between the psychological state and drawing feature. Specifically, we extract the key features including size, position, and shadow of the drawing, and then combine these features to construct a psychological state classifier. The proposed method can effectively screen out negative drawings for further diagnosis and treatment. Experiments are carried out on a builded dataset with the drawings from a psychological testing center of college. Experimental results demonstrate the effect and superiority of the proposed method.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126586219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

EthicAmanuensis: supporting machine learning practitioners making and recording ethical decisions EthicAmanuensis:支持机器学习从业者做出和记录道德决策

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00195

Dave Murray-Rust, K. Tsiakas

引用次数: 1

Real-time classification of handball game situations 手球比赛情况的实时分类

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00106

Bruno Cabado, B. Guijarro-Berdiñas, Emilio J. Padrón

引用次数: 0

Domain-adaptive Graph based on Post-hoc Explanation for Cross-domain Hate Speech Detection 基于事后解释的域自适应图跨域仇恨语音检测

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00192

Yushan Jiang, Bin Zhou, Xuechen Zhao, Jiaying Zou, Feng Xie, Liang Li

引用次数: 0

Exploring Policy Diversity in Parallel Actor-Critic Learning 平行行动者-批判学习中的政策多样性探索

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00182

Yanqiang Zhang, Yuanzhao Zhai, Gongqian Zhou, Bo Ding, Dawei Feng, Songwang Liu

{"title":"Exploring Policy Diversity in Parallel Actor-Critic Learning","authors":"Yanqiang Zhang, Yuanzhao Zhai, Gongqian Zhou, Bo Ding, Dawei Feng, Songwang Liu","doi":"10.1109/ICTAI56018.2022.00182","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00182","url":null,"abstract":"Exploration is a critical challenge for deep reinforcement learning methods. Although existing works such as actor-critic algorithms have made much progress, most still suffer from the sample inefficiency problem in complex environments where rewards are sparse. Parallel sampling, which uses multiple actors with the same policy interacting with the environment, is an effective approach to improve sample efficiency. However, parallel parameter-sharing actors collect similar samples, which generally hinders the improvement of the overall exploration process. In this paper, we propose a Policy Diversity enhanced approach for parallel Actor-Critic (PDAC). Specifically, we extend the parallel actor-critic architecture to the PDAC framework composed of a shared critic and parallel distinct actors. Then we introduce the KL-divergence of the action probability distribution between parallel actors as the intrinsic reward to encourage actors to explore diverse strategies. We evaluate our approach in multiple challenging procedurally-generated tasks and compare it with state-of-the-art algorithms. Experiments show that PDAC makes significant progress in the comparison, in terms of cumulative rewards and sample efficiency.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"64 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subspace-Focused Search Method for Optimal Coalition Structure Generation 最优联盟结构生成的子空间聚焦搜索方法

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00217

Redha Taguelmimt, S. Aknine, Djamila Boukredera, Narayan Changder

引用次数: 1

Dual Autoencoder Enhanced Subgraph Pattern Mining for Cognitive Diagnosis 基于双自编码器的认知诊断增强子图模式挖掘

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00086

Haodong Meng, Changzhi Chen, Hongyu Yi, Xiaofeng He

{"title":"Dual Autoencoder Enhanced Subgraph Pattern Mining for Cognitive Diagnosis","authors":"Haodong Meng, Changzhi Chen, Hongyu Yi, Xiaofeng He","doi":"10.1109/ICTAI56018.2022.00086","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00086","url":null,"abstract":"In adaptive learning, Cognitive diagnosis aims to discover students' knowledge state on different knowledge con-cepts and predict their future performance. Most previous methods consider more on students' own answering history and rarely model the the impact brought by students with similar answering behaviors explicitly. This collaborative information among students is helpful for students who lack sufficient historical logs. In this paper, we propose a new cognitive diagnosis method called Dual Autoencoder Enhanced Subgraph Pattern Mining(DASPM) for Cognitive Diagnosis, which incorporates collaborative information among students into the cognitive di-agnosis process to obtain more accurate predictions. Specifically, we use a graph neural network to capture collaborative pattern on the student-exercise bipartite graph. In order to filter out the interference of irrelevant information, we design a sub graph extraction algorithm that separates local parts around the target student-exercise pair from global graph based on the correlation between exercises. In addition, we utilize a dual autoencoder module to encode students and exercises to enhance the initial representation of nodes in the sub graph. Extensive experiments on multiple datasets show the effectiveness of our proposed method.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bilateral-Branch Network for Imbalanced Visual Regression 不平衡视觉回归的双侧分支网络

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00028

Rongjiao Liang, Guixian Zhang, Kui Zhang, Zhi Lei, Shichao Zhang

引用次数: 0

FIXC: A Method for Data Distribution Shifts Calibration via Feature Importance FIXC:一种基于特征重要性的数据分布偏移校准方法

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00030

Liu Zhendong, Wenyu Jiang, Yan Zhang, Chongjun Wang

{"title":"FIXC: A Method for Data Distribution Shifts Calibration via Feature Importance","authors":"Liu Zhendong, Wenyu Jiang, Yan Zhang, Chongjun Wang","doi":"10.1109/ICTAI56018.2022.00030","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00030","url":null,"abstract":"With the rapid development of Artificial Intelligence (AI), a long line of past papers have shown concerns about the data distribution shifts problem in image classification models via deep learning. Moreover, there is also an Out-of-Distribution (OOD) problem in perturbation-based explanation methods for DNNs ineXplainable Artificial Intelligence (XAI), because the generated perturbation samples may be not the same distribution as the original dataset. We explore the limitations of post-hoc Learning to Explain (L2X) explanation methods that use approximators to mimic the behavior of DNNs. We propose a training pipeline called Feature Importance eXplanation(-based) Calibration (FIXC), which efficiently extracts feature importance without using imitations of existing DNNs. We use feature importance as additional information to calibrate data distribution shifts. The evaluation of the corrupted dataset and DNNs benchmarks shows that the FIXC effectively improves the classification accuracy of corrupted images. Experiments on popular vision datasets show that the FIXC outperforms state-of-the-art methods on calibration metrics While the training pipeline provides a calibrated feature importance explanation. We also provide an analysis of our method based on game interaction theory.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125035744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0