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18th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Paulik, Robert Kozlovszky, Miklos Molnar, Bela Obuda Univ 3DHISTECH Ltd Budapest Hungary Obuda Univ BioTech Res Ctr Budapest Hungary 3DHISTECH Ltd Image Anal Dept Budapest Hungary

ISBN: (纸本)9798350329537;9798350329520

With the digitalization of routine histology and pathology, there has emerged a need for automated histological instruments such as automated microdissectors, staining-coverslipper machines, and high-performance slide microscope scanners. Essential to the seamless integration of these tools are registration solutions that restore the tissue orientation lost between different workflow stages. In this way, samples can be evaluated together, accelerating and facilitating the diagnosis process. image registration is one of the most researched topics today, with state-of-the-art machine learning solutions like SuperPoint and SuperSet effectively able to match similar images. However, they struggle to find correspondences in a multimodal environment between tissue block and section images. Our proposed solution addresses this issue by decomposing the registration process into tissue recognition and the matching of tissue regions.

关键词： digital pathology microscope scanner image registration digital slide tissue block section tissue matching

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COLLABORATIVE intelligence FOR VISION TRANSFORMERS: A TOKEN SPARSITY-DRIVEN EDGE-CLOUD FRAMEWORK 31

COLLABORATIVE INTELLIGENCE FOR VISION TRANSFORMERS: A TOKEN ...

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2024 International Conference on image processing

作者： Busto, Monikka Roslianna Enomoto, Shohei Eda, Takeharu NTT Software Innovat Ctr Tokyo Japan

ISBN: (纸本)9798350349405;9798350349399

Collaborative intelligence (CI) streamlines DNN deployment in edge-cloud infrastructure by optimizing workload between the edge and the cloud. It leverages data sparsity for both data compression for cloud transmission and the reduction of the overall computational cost of the system. Despite the rising popularity of the Vision Transformer (ViT), its higher computational overhead poses challenges for edge-cloud deployment compared to CNNs. Existing CI methods favor CNNs, utilizing feature map sparsity. In contrast, ViTs exploit token-based sparsity, complicating the direct application of CNN-optimized CI methods. This motivates us to propose a novel CI approach exploiting the token sparsity of ViT. We propose an offloading policy network, which computes scores that reveal the tokens' relevance to the task before inference, effectively using sparse tokens in the offloaded data improving compression rate and computational cost for an edge-cloud system. Our method shows 41.98-45.75% computational cost reduction of ViT while maintaining accuracy degradation within 1.96-3.10 points and achieving a compression rate up to 36.85%.

关键词： Collaborative intelligence Vision Transformer Token Sparsity Edge Computing

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Ultra-Lightweight Network for Medical image Segmentation Inspired by Bio-Visual Interaction

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 3486-3497页

作者： Cai, Zhefei Fan, Yingle Zhu, Minwei Fang, Tao Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Univ Miami Dept Biomed Engn Miami FL 33124 USA

Computer-aided medical image segmentation helps to assist physicians in locating lesion area for the subsequent diagnosis and treatment. Due to the irregular shape of the target and the uneven sample size between the target and the background area, automatic segmentation of medical images is a challenging task. Many CNN-Based, Transformer-Based models deepen the number of network layers or introduce complex modules in order to improve the segmentation accuracy. Limited by the computational resources, these types of large models are not suitable for the actual clinical environment. Inspired by the rapidity, accuracy, and low consumption characteristics of bio-visual processing, the Ultra-Lightweight Network Inspired by Bio-Visual Interaction (BVI-Net) is constructed in this paper. The Global Pathway is constructed by simulating the dorsal stream, in order to extract global features rapidly, and the Local Pathway is constructed by simulating the ventral stream, in order to process local features finely. At the same time, the skip connection module integrating Graph Convolutional Network (GCN) attention mechanism is constructed to simulate the synchronous integration ability of the visual pathway for multi-level features. The International Skin Imaging Collaboration (ISIC) dataset, the Liver Tumor Segmentation (LiTS) dataset, and the Brain Tumor Segmentation Challenge (BraTS) dataset are used for experiments. The BVI-Net proposed in this paper requires only 0.026M parameters to achieve the excellent performance in three representative medical image segmentation datasets, which has certain advantages over state-of-the-art (SOTA) methods. The biological vision mechanism and the artificial intelligence algorithm are integrated in this paper, which provides new ideas for the construction of biological vision-guided deep learning models and promotes the development of biomimetic computational vision.

关键词： Feature extraction image segmentation Transformers Kernel Accuracy Visualization computational modeling Biological system modeling Lesions Streaming media Biological visual pathway dual encoder architecture GCN attention mechanism state space models medical image segmentation

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Does computational thinking really have an impact on academic performance? A systematic review 10

Does computational thinking really have an impact on academi...

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10th International symposium on Educational Technology (ISET)

作者： Zhou, Pinghong Tang, Yumin Zhang, Yi Yu, Yue Li, Yixin Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China

ISBN: (纸本)9798350361414

This study is a systematic review of the effects of computational thinking on students' academic performance from 2006 to 2023. The main purpose of this study is to explore whether computational thinking can promote students' academic performance and the characteristics of such research. After literature collection and screening from three databases, a total of 23 articles were included. According to the coding table, the literature was coded in five aspects, including basic information, sample, research, computational thinking and academic performance. Then, the publication trend, research types, effect results, computational thinking measurement and the characteristics of academic achievement evaluation of the impact of computational thinking and academic achievement are reviewed. It is found that computational thinking has a better effect on students' academic achievement, and finally some suggestions are put forward.

关键词： computational thinking systematic review academic achievement

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QGIP: A Framework Bridging Quantum Grayscale image processing and Applications 22

QGIP: A Framework Bridging Quantum Grayscale Image Processin...

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22nd ieee International symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Che, Xilong Zhang, Jiale Chen, Shuo Peng, Shun Hu, Juncheng Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331509712

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n × 2n after a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal. © 2024 ieee.

关键词： image reconstruction

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Adaptive Bit Selection for Scalable Deep Hashing

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ieee TRANSACTIONS ON image processing 2025年 34卷 1048-1059页

作者： Wang, Min Zhou, Wengang Yao, Xin Li, Houqiang Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230000 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci MoE Key Lab Brain Inspired Intelligent Percept & C Hefei 230027 Peoples R China

Deep Hashing is one of the most important methods for generating compact feature representation in content-based image retrieval. However, in various application scenarios, it requires training different models with diversified memory and computational resource costs. To address this problem, in this paper, we propose a new scalable deep hashing framework, which aims to generate binary codes with different code lengths by adaptive bit selection. Specifically, the proposed framework consists of two alternative steps, i.e., bit pool generation and adaptive bit selection. In the first step, a deep feature extraction model is trained to output binary codes by optimizing retrieval performance and bit properties. In the second step, we select informative bits from the generated bit pool with reinforcement learning algorithm, in which the same retrieval performance and bit properties are directly used in computing reward. The bit pool can be further updated by fine-tuning the deep feature extraction model with more attention on the selected bits. Hence, these two steps are alternatively iterated until convergence is achieved. Notably, most existing binary hashing methods can be readily integrated into our framework to generate scalable binary codes. Experiments on four public image datasets prove the effectiveness of the proposed framework for image retrieval tasks.

关键词： Quantization (signal) Binary codes Costs Measurement Feature extraction computational modeling Databases Representation learning Training Hash functions Adaptive bit selection deep hashing scalable hashing image retrieval

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HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation

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ieee TRANSACTIONS ON image processing 2024年 33卷 4202-4214页

作者： Xu, Guoan Jia, Wenjing Wu, Tao Chen, Ligeng Gao, Guangwei Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Honor Device Co Ltd Shenzhen 518040 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China Minist Educ Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

Both Convolutional Neural Networks (CNNs) and Transformers have shown great success in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer models to capture both local and global context interactions. However, there is still room for enhancement, particularly when considering constraints on computational resources. In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges. Specifically, we design a Hierarchy-Aware Pixel-Excitation (HAPE) module for adaptive multi-scale local feature extraction. During the global perception modeling, we devise an Efficient Transformer (ET) module streamlining the quadratic calculations associated with traditional Transformers. Moreover, a correlation-weighted Fusion (cwF) module selectively merges diverse feature representations, significantly enhancing predictive accuracy. HAFormer achieves high performance with minimal computational overhead and compact model size, achieving 74.2% mIoU on Cityscapes and 71.1% mIoU on CamVid test datasets, with frame rates of 105FPS and 118FPS on a single 2080Ti GPU. The source codes are available at https://***/XU-GITHUB-curry/HAFormer.

关键词： Transformers computational modeling Feature extraction Semantics Accuracy Task analysis Semantic segmentation lightweight multi-scale feature extraction local and global context

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image Reconstruction for Accelerated MR Scan With Faster Fourier Convolutional Neural Networks

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ieee TRANSACTIONS ON image processing 2024年 33卷 2966-2978页

作者： Liu, Xiaohan Pang, Yanwei Sun, Xuebin Liu, Yiming Hou, Yonghong Wang, Zhenchang Li, Xuelong Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Brain Inspired Intelligence Techno Tianjin 300072 Peoples R China Tiandatz Technol Co Ltd Tianjin 301723 Peoples R China Capital Med Univ Beijing Friendship Hosp Beijing 100050 Peoples R China China Telecom Corp Ltd Inst Artificial Intelligence TeleAI Beijing 100033 Peoples R China

High quality image reconstruction from undersampled ${k}$ -space data is key to accelerating MR scanning. Current deep learning methods are limited by the small receptive fields in reconstruction networks, which restrict the exploitation of long-range information, and impede the mitigation of full-image artifacts, particularly in 3D reconstruction tasks. Additionally, the substantial computational demands of 3D reconstruction considerably hinder advancements in related fields. To tackle these challenges, we propose the following: 1) A novel convolution operator named Faster Fourier Convolution (FasterFC), aims at providing an adaptable broad receptive field for spatial domain reconstruction networks with fast computational speed. 2) A split-slice strategy that substantially reduces the computational load of 3D reconstruction, enabling high-resolution, multi-coil, 3D MR image reconstruction while fully utilizing inter-layer and intra-layer information. 3) A single-to-group algorithm that efficiently utilizes scan-specific and data-driven priors to enhance ${k}$ -space interpolation effects. 4) A multi-stage, multi-coil, 3D fast MRI method, called the faster Fourier convolution based single-to-group network (FAS-Net), comprising a single-to-group ${k}$ -space interpolation algorithm and a FasterFC-based image domain reconstruction module, significantly minimizes the computational demands of 3D reconstruction through split-slice strategy. Experimental evaluations conducted on the NYU fastMRI and Stanford MRI Data datasets reveal that the FasterFC significantly enhances the quality of both 2D and 3D reconstruction results. Moreover, FAS-Net, characterized as a method that can achieve high-resolution (320, 320, 256), multi-coil, (8 coils), 3D fast MRI, exhibits superior reconstruction performance compared to other state-of-the-art 2D and 3D methods.

关键词： Three-dimensional displays image reconstruction Convolution Magnetic resonance imaging Feature extraction Task analysis Interpolation 3D MRI reconstruction magnetic resonance imaging faster Fourier convolution

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Efficient Swin Transformer for Remote Sensing image Super-Resolution

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ieee TRANSACTIONS ON image processing 2024年 33卷 6367-6379页

作者： Kang, Xudong Duan, Puhong Li, Jier Li, Shutao Hunan Univ Sch Robot Changsha 410082 Peoples R China Key Lab Visual Percept & Artificial Intelligence H Changsha 410082 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

Remote sensing super-resolution (SR) technique, which aims to generate high-resolution image with rich spatial details from its low-resolution counterpart, play a vital role in many applications. Recently, more and more studies attempt to explore the application of Transformer in remote sensing field. However, they suffer from the high computational burden and memory consumption for remote sensing super-resolution. In this paper, we propose an efficient Swin Transformer (ESTNet) via channel attention for SR of remote sensing images, which is composed of three components. First, a three-layer convolutional operation is utilized to extract shallow features of the input low-resolution image. Then, a residual group-wise attention module is proposed to extract the deep features, which contains an efficient channel attention block (ECAB) and a group-wise attention block (GAB). Finally, the extracted deep features are reconstructed to generate high-resolution remote sensing images. Extensive experimental results proclaim that the proposed ESTNet can obtain better super-resolution results with low computational burden. Compared to the recently proposed Transformer-based remote sensing super-resolution method, the number of parameters is reduced by 82.68% while the computational cost is reduced by 87.84%. The code of the proposed ESTNet will be available at https://***/PuhongDuan/ESTNet for reproducibility.

关键词： Remote sensing Superresolution Feature extraction image reconstruction computational modeling Current transformers Deep learning Sensors Imaging Spatial resolution Remote sensing image image super-resolution Swin Transformer channel attention

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A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours Using Different Modalities

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年第2期9卷 1216-1225页

作者： Sahu, Adyasha Das, Pradeep Kumar Paul, Indraneel Meher, Sukadev VIT Bhopal Univ Sch Comp Sci Engn & Artificial Intelligence Bhopal 466114 India Vellore Inst Technol Sch Elect Engn Vellore 632014 India Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 India

Nowadays, deep convolutional neural networks (DCNNs) are the focus of substantial research for classification and detection applications in medical image processing. However, the limited availability and unequal data distribution of publicly available datasets impede the broad use of DCNNs for medical image processing. This work proposes a novel deep learning-based framework for efficient detection of brain tumors across different openly accessible datasets of different sizes and modalities of images. The introduction of a novel end-to-end Cumulative Learning Strategy (CLS) and Multi-Weighted New Loss (MWNL) function reduces the impact of unevenly distributed datasets. In the suggested framework, the DCNN model is incorporated with regularization, such as DropOut and DropBlock, to mitigate the problem of over-fitting. Furthermore, the suggested augmentation approach, Modified RandAugment, successfully deals with the issue of limited availability of data. Finally, the employment of K-nearest neighbor (KNN) improves the classification performance since it retains the benefits of both deep learning and machine learning. Moreover, the effectiveness of the proposed framework is also validated over small and imbalanced datasets. The proposed framework outperforms others with an accuracy of up to $ 99.70\%$.

关键词： Tumors Databases Accuracy Magnetic resonance imaging Training Brain modeling Nearest neighbor methods Brain tumors convolutional neural network data augmentation deep learning machine learning MRI

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