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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Hongjie He, Linchao Zhang, Mingqin Chen, Dongdong Luo, Kunming Luo, Mengting Zhou, Ji-Zhe Chen, Hu Lv, Jiancheng College of Computer Science Sichuan University Chengdu China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China Heriot-Watt University Edinburgh United Kingdom Hong Kong University of Science and Technology Hong Kong Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China College of Computer Science Sichuan University China

ISBN: (纸本)9798400706868

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources. © 2024 ACM.

关键词： Image reconstruction

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Deep Multiscale Multi-Instance Networks with Regional Scoring for Mammogram Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2022年第3期3卷 485-496页

作者： Liu, Wenjie Shu, Xin Zhang, Lei Li, Dong Lv, Qing Sichuan University Machine Intelligence Laboratory College of Computer Science Chengdu610065 China Sichuan University Department of Galactophore Surgery West China Hospital Chengdu610041 China

Mammography is one of the most commonly used methods for breast cancer screening. Most mammogram classification or segmentation models are trained with additional manual segmentation annotation data, which is difficult to obtain. Meanwhile, these models rarely take into account the fact that lesion features may vary at different scales. Moreover, most models have difficulty in quantifying the effect of single lesion regions on prediction results. To solve the above problems, we propose deep multiscale multi-instance networks with regional scoring. This model classifies mammograms automatically by image labels without using any other annotation information during the training procedure. The proposed multiscale structures include large scale, medium scale, and small scale, and they can extract features of mammograms from global to local. Because there may be more than one breast lesion region in a mammogram, the proposed multi-instance structure divides the image into multiple regions and selects few regions of interest to represent the whole image together. The regional scoring mechanism quantifies the contribution of each region to the prediction results, and thus, makes the results interpretable. In addition, the proposed method can roughly localize the lesion area by the score of each region. The experimental results on a publicly available dataset and a private dataset have shown that the proposed method achieved the highest AUC on both datasets compared with the state-of-the-art models. © 2020 IEEE.

关键词： Mammography

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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Faster approximate subgraph counts with privacy 37

Faster approximate subgraph counts with privacy

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Nguyen, Dung Srinivasan, Venkatesh Halappanavar, Mahantesh Vullikanti, Anil Department of Computer Science UVA Biocomplexity Institute Initiative University of Virginia United States Department of Mathematics and Computer Science Santa Clara University United States Data Sciences and Machine Intelligence Group Pacific Northwest National Laboratory United States

ISBN: (纸本)9781713899921

One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as smooth sensitivity and higher-order local sensitivity have been shown to give significantly better accuracy. However, all these alternatives to global sensitivity become computationally very expensive, and to date efficient polynomial time algorithms are known only for few selected subgraphs, such as triangles, k-triangles, and k-stars. In this paper, we show that good approximations to these sensitivity metrics can be still used to get private algorithms. Using this approach, we much faster algorithms for privately counting the number of triangles in real-world social networks, which can be easily parallelized. We also give a private polynomial time algorithm for counting any constant size subgraph using less noise than the global sensitivity;we show this can be improved significantly for counting paths in special classes of graphs. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Polynomial approximation

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Verified localization and pharmacognosy of herbal medicinal plants in a combined network framework

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computers in Biology and Medicine 2024年 174卷 108467-108467页

作者： Sharafudeen, Misaj S.S., Vinod Chandra A.L., Aswathy Navas, Asif K.N., Vismaya Machine Intelligence Research Laboratory Department of Computer Science University of Kerala India School of Computer Sciences Mahatma Gandhi University Kottayam India

Pharmacognosy from medicinal plants involves the scientific domain of medicinal compounding based on their medicinal properties. Accurate identification of medicinal plants is crucial, especially by examining their leaves. Choosing the wrong plant species for medicinal preparations can have adverse side effects. This study presents a Human-Centered Artificial intelligence approach for medicinal plant identification, combining a YOLOv7-based Leaf Localizer with a leaf Class Verifier based on DenseNet through a Confidence Score Analyser algorithm. The Confidence Score Analyser ensures reliability by evaluating predicted categories against predefined thresholds, and the ensemble technique through majority voting enhances robustness. An average performance gain of 25.66% sensitivity is observed when comparing the YOLO object detection model with 77.45% precision to the YOLO integrated with the class verifier model with 97.33% precision. Consistent sensitivities are achieved through the ensemble technique, showcasing robustness across diverse scenarios. The final step incorporates automated textual and audio pharmacognosy information about the identified medicinal plant properties and their utility. Real-time applicability as a smart phone application makes this approach invaluable for medicinal plant collectors and experts. © 2024 Elsevier Ltd

关键词： Object detection

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Akbari, Akbar Sheikh Kumar, Arvind Reddy, B Ramachandra Singh, Koushlendra Kumar Takei, Masahiro Leeds Beckett University United Kingdom National Institute of Technology Machine Vision and Intelligence Lab Jharkhand Jamshedpur India Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur India Chiba University Department of Mechanical Engineering Chiba Japan

ISBN: (纸本)9798350378214

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets. © 2024 IEEE.

关键词： Lung cancer

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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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Multimodal deep learning approach for identifying and categorizing intracranial hemorrhage

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Multimedia Tools and Applications 2023年第27期82卷 42945-42960页

作者： S, Anand Hareendran SS, Vinod Chandra Department of Computer Science and Engineering Muthoot Institute of Technology and Science Kochi India Machine Intelligence Laboratory Department of Computer Science University of Kerala Thiruvananthapuram India

Intracranial haemorrhage is a life threatening emergency where acute bleeding occurs inside the skull or brain. Studies show that 37% to 41% of bleeding stroke causes death within 30 days. Although minor bleeding is usually less severe, the location where the bleed occurs may turn it critical. Early detection of intracranial bleeding becomes an important activity in the event of diagnosis and selection of the most appropriate treatment option to save the patient’s life. Diagnosis of the bleeding is a complex and time consuming process. Neurologic consequences can vary broadly from headache to death depending on size, type, and location of bleeding. Radiologist with the help of computer Aided Diagnosis(CAD) system diagnose bleeding, possible cause of the bleeding and determines immediate treatment plans. This work proposes an ensemble based deep learning approach to identify and categorize intracranial hemorrhage from Computed Tomography(CT) scan images. The work explores several transfer learning methods for the classification of intracranial hemorrhage and two ensemble strategies for detection purposes. Initially, five highly efficient models were selected which are then used for the ensemble. The models were individually trained and evaluated using the well known Radiological Society of North America(RSNA) challenge 2019 dataset. This study uses a soft voting method by adding the estimated possibilities for individual models. The stacked generalization ensemble method uses a new model to learn how to better integrate predictions from two or more transfer learning models trained in the RSNA database. The soft voting ensemble achieved a test accuracy score of 91.09% and the stacked generalization ensemble achieved a test accuracy score of 94.60%.

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LC2R-ViT: Long-Range Cross-Residual vision Transformer for Medical Image Classification

LC2R-ViT: Long-Range Cross-Residual Vision Transformer for M...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zhang, Zhenwei Zhang, Lei Wang, Lituan Zhong, Ke Huang, Haiying Sichuan University College of Computer Science Machine Intelligence Laboratory Chengdu China Sichuan University West China Second University Hospital Information Management Department Chengdu China

ISBN: (纸本)9798350309003

With the development of Convolutional Neural Network (CNN) as well as the rise of vision Transformer (ViT), approaches based on these two series are widely used in various visual tasks. However, the performance of CNN-based approaches and transformer-based approaches can be very different for different datasets. In this work, we firstly investigate the applicability of CNN-based and transformer-based approaches for different datasets. Furthermore, in order to integrate advantages of CNN and transformer, a novel network named LC2R-ViT is designed to introduce the ability of extracting local but detailed features of CNN into the vision Transformer through a long-range cross-residual mechanism. To evaluate the effectiveness of the proposed LC2R-ViT, MedMNIST dataset that with the diversity among the tasks, the data scales and the modalities is employed. The comparative experiments have conducted and the experimental results have shown that the proposed LC2R-ViT can outperform CNN and vision Transformer on the most subsets of MedMNIST dataset. That means the proposed LC2R-ViT can improve the generalization of transformer-based approaches by introducing the CNN. © 2023 IEEE.

关键词： Convolutional neural networks

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