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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

ISBN: (纸本)9798331508333

The proceedings contain 17 papers. The topics discussed include: transforming medical practice: harnessing the power of big data and machine learning for predictive precision medicine;algebraic reproduction of triage decision making on simple heuristics of Boolean multivalued logic;deep learning on Hester Davis scores for inpatient fall prediction;comparative analysis and evaluation of well-being activity-infused fine-tuned language models with benchmark models;application of fuzzy-based uncertainty in cardiac MRI segmentation;images of stress: investigating image encodings and transfer learning for stress detection;training deep neural networks with multi-objective Adam optimizer for medical image classification;and deep-learning classifiers for small data orthopedic radiology.

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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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Generative Artificial intelligence for Hyperspectral Sensor Data: A Review

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution Machine learning Neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

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CVIformer: Cross-View Interactive Transformer for Efficient Stereoscopic image Super-Resolution

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年第2期9卷 1107-1118页

作者： Zhang, Dongyang Liang, Shuang He, Tao Shao, Jie Qin, Ke Univ Elect Sci & Technol China Inst Intelligent Comp Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

Inspired by the great success of the Transformer in computer vision, some works have started to explore the use of the Transformer for super-resolution (SR). However, with regard to stereoscopic SR, which aims to recover details from input pairs, how to efficiently integrate cross-view interactions into the Transformer architecture is still an ongoing development. Additionally, most existing stereoscopic SR methods only adopt a parallax mechanism in the middle of the network, and another issue is that the feature correlation from different viewpoints inevitably weakens as the network depth increases. To address these issues, we first utilize an efficient residual transformer block (ERTB) as the backbone for long-range intra-view feature extraction. Subsequently, we propose a novel multi-Dconv cross attentive block (MCAB) to enhance the cross-view interactions at the rear part of the Transformer architecture. Notably, the proposed MCAB promotes feature fusion from two viewpoints by employing bidirectional cross-attention, as opposed to an unidirectional flow from left to right or vice versa. This approach results in an efficient cross-view interaction from both branches. By leveraging the advantages of the proposed ERTB and MCAB, we introduce an efficient cross-view interaction Transformer (CVIformer) for stereoscopic SR. This architecture is capable of incorporating long-range intra-view and cross-view information with an acceptable computational overhead. Without excessive complexity, extensive experiments conducted on four public datasets demonstrate that our model achieves state-of-the-art results using only 1.17 million parameters, with approximately a 40% reduction in parameters compared to leading methods like iPASSR.

关键词： Transformers Stereo image processing Feature extraction Superresolution Spatial resolution Cameras Task analysis Stereoscopic image processing lightweight transformer super-resolution (SR)

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Evolutionary Neural Architecture Search Framework With Masked Encoding Mechanism for Point Cloud Registration

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年

作者： Wu, Yue Gong, Peiran Yuan, Yongzhe Gong, Maoguo Ma, Wenping Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010022 Peoples R China Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Deep learning-based point cloud registration (PCR) has received increasing attention in the field of computer vision. However, the lack of expertise in deep neural network architecture design remains a large obstacle for end-users with registration needs. While neural architecture search (NAS) indeed addresses the architecture automation design problem and has undergone substantial development, the majority of existing NAS approaches are tailored for image processing. Because of significant differences in data characteristics and network structures, directly transferring the existing NAS approaches for PCR is infeasible. Hence, this work proposes an evolutionary neural architecture search framework for PCR, which can fit the different types of PCR networks. The framework, equipped with the designed masked encoding mechanism, can seamlessly integrate with the majority of evolutionary computation-based optimization algorithms, thereby reducing the additional cost of operator design. Based on the above framework, the users can freely select the advanced network components and optimization algorithms depending on their needs. Experimental results on synthetic and real-world datasets, ModelNet40 and KITTI, demonstrate the superiority of the proposed framework in terms of registration precision and architecture complexity.

关键词： Feature extraction Point cloud compression Encoding Computer architecture Data mining Accuracy Three-dimensional displays Decoding Transformers Neural architecture search Point cloud registration evolutionary neural architecture search novel encoding mechanism

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Efficient image Super-Resolution With Feature Interaction Weighted Hybrid Network

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ieee Transactions on Multimedia 2025年 27卷 2256-2267页

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing100080 China Shanghai University School of Communication and Information Engineering Shanghai200444 China Nanjing University of Posts and Telecommunications IVIPLab Institute of Advanced Technology Nanjing210046 China Ministry of Education Key Laboratory of Artificial Intelligence Shanghai200240 China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou215006 China Nanjing University of Science and Technology School of Computer Science and Technology Nanjing210094 China Westlake University Research Center for Industries of the Future School of Engineering Hangzhou310024 China OPPO Research SeattleWA98101 United States National Tsing Hua University Department of Electrical Engineering Institute of Communications Engneering Hsinchu300044 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. © 1999-2012 ieee.

关键词： Transformers Convolutional neural networks computational modeling Feature extraction Training Superresolution Adaptation models Computer architecture Telecommunications image reconstruction

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Simultaneously Learning Deep Quaternion Reconstruction and Noise Convolutional Dictionary for Color image Denoising

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年第2期9卷 1766-1779页

作者： Zhou, Zheng Chen, Yongyong Zhou, Yicong Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China

Recently, many deep convolutional dictionary learning-based methods, integrating the traditional image representation methods with deep neural networks, have achieved great success in various image processing tasks. However, the existing approaches can be further improved with the following considerations: (1) They congenitally suffer from the high cross-channel correlation loss for color image processing tasks since they usually treat each color channel independently, not in a whole perspective. (2) They only build up a single reconstruction dictionary learning model to directly approximate images using several single dictionary atoms, which cannot make full use of the representative ability of the model. In this paper, we propose a simultaneously learning deep quaternion reconstruction and noise convolutional dictionary model. To fully explore the cross-channel correlation, we use the quaternion method to process the color image in a holistic way. An adaptive attentional weight of reconstruction and noise learning module is also developed for the optimal combination between reconstruction and noise learning. Experimental results for synthesis and real color image denoising have demonstrated the superiority of the proposed method over other state-of-the-art methods.

关键词： Quaternions Dictionaries Color image reconstruction image color analysis Convolution Noise reduction Deep unfolding learning color image denoising quaternion

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LYT-NET: Lightweight YUV Transformer-based Network for Low-light image Enhancement

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ieee Signal processing Letters 2025年 32卷 2065-2069页

作者： Brateanu, Alexandru Balmez, Raul Avram, Adrian Orhei, Ciprian Ancuti, Cosmin University of Manchester United Kingdom University Politehnica Timisoara Romania West University of Timisoara Romania

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement. LYT-Net consists of several layers and detachable blocks, including our novel blocks-Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)-along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel Y as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. © 1994-2012 ieee.

关键词： Transformers Feature extraction image enhancement image restoration Training image color analysis Complexity theory Head computational modeling Convolution

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A Lightweight Model for Fast and Detail-Preserving Human Segmentation in Video

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年

作者： Zhang, Xiaoyao Yao, Lan Zeng, Feng Cent South Univ Sch Comp Scienceand Engn Changsha 410017 Peoples R China Hunan Univ Sch Math Changsha 410012 Peoples R China

With the rapid development of artificial intelligence, human segmentation in video is becoming increasingly important in the field of computer vision. However, existing segmentation models suffer from inaccurate segmentation, slow processing, and large model size that limit their deployment on resource-constrained devices. To this end, we propose a lightweight model called Efficient Memory Aggregation U-shaped Network (EMAUnet) for human segmentation in video, which is based on a traditional U-shaped network and attention mechanism. In EMAUnet, memory modules are combined with segmentation modules, enabling end-to-end learning of semantic extraction patterns for human images. Mobile-inverse bottleneck convolution (MBConv) is used as the network backbone that has relatively few parameters and computational complexity. Inverted sub-pixel down-sampling (ISP) is proposed to minimize information loss and achieve detail-preserving of segmentation. Coordinate attention (CA) is adopted to precisely locate the portrait area. Moreover, bidirectional memory update (BMU) and memory update trigger (MUT) are proposed to improve memory resource utilization and reduce unnecessary computation. Experimental results show that, compared with the classic model ISNet, EMAUnet has the mIoU, FPS and pixel accuracy increased by 2.3%, 25.0% and 1.8%, respectively, while the amount of parameters and the size of model decreased by 33.3% and 45.7%, respectively.

关键词： image segmentation computational modeling Accuracy Semantics Object segmentation Streaming media Real-time systems Memory management Convolution Training Video human segmentation lightweight model U-shaped network attention mechanism

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Energy-Efficient Deep Learning for Cloud Detection Onboard Nanosatellite

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9968-9985页

作者： Khalil, Imane Daghouri, Amina Chanoui, Mohammed Alae Guennoun, Zouhair Addaim, Adnane Mohammed V Univ Univ Ctr Res Space Technol Mohammadia Sch Engineers Rabat Morocco

Deep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance. Traditional methods of image capture, onboard storage, and transmission face challenges such as bandwidth limitations and the transmission of cloud-obstructed images, highlighting the need for efficient onboard artificial intelligence. However, energy consumption remains a critical consideration for onboard processing, particularly in nanosatellites where resource constraints are significant. To address these challenges, we propose an optimized SegNet-based deep learning model implemented on the STM32H7 microcontroller for real-time cloud detection, designed to operate within the nanosatellite's strict energy budget. This work, conducted within the project of the UM5-EOSat nanosatellite mission, utilized captured images from the Gecko imager for model evaluation. The customized SegNet architecture, tailored with minimal kernels and layers, achieved an accuracy of 93.50%, effectively balancing performance and computational efficiency. Quantization further optimized energy consumption, achieving a reduction of 82.2% at 280 MHz. The quantized model demonstrated a memory footprint of 304 KB RAM and 110 KB Flash memory, with an inference time of 0.21 s and an energy consumption of 31.41 mJ, ensuring compatibility with the nanosatellite's resource constraints.

关键词： computational modeling Accuracy Energy consumption Clouds Memory management Artificial intelligence Deep learning computational efficiency Power demand Microcontrollers Cloud detection deep learning energy consumption nanosatellite TinyML

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