Journal of King Saud University-Computer and Information Sciences最新文献_第5页

Deep indoor illumination estimation based on spherical gaussian representation with scene prior knowledge 基于球形高斯表示和场景先验知识的深度室内光照度估计

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-23 DOI: 10.1016/j.jksuci.2024.102222

Chao Xu , Cheng Han , Huamin Yang , Chao Zhang , Shiyu Lu

{"title":"Deep indoor illumination estimation based on spherical gaussian representation with scene prior knowledge","authors":"Chao Xu , Cheng Han , Huamin Yang , Chao Zhang , Shiyu Lu","doi":"10.1016/j.jksuci.2024.102222","DOIUrl":"10.1016/j.jksuci.2024.102222","url":null,"abstract":"<div><div>High dynamic range (HDR) illumination estimation from a single low dynamic range image is a critical task in the fields of computer vision, graphics and augmented reality. However, directly learning the full HDR environment map or parametric lighting information from a single image is extremely difficult and inaccurate. As a result, we propose a two-stage network approach for illumination estimation that integrates spherical gaussian (SG) representation with scene prior knowledge. In the first stage, a convolutional neural network is utilized to generate material and geometric information about the scene, which serves as prior knowledge for lighting prediction. In the second stage, we model indoor environment illumination using 128 SG functions with fixed center direction and bandwidth, allowing only the amplitude to vary. Subsequently, a Transformer-based lighting parameter regressor is employed to capture the complex relationship between the input images with scene prior information and its SG illumination. Additionally, we introduce a hybrid loss function, which combines a masked loss for high-frequency illumination with a rendering loss for improving the visual quality. By training and evaluating the lighting model on the created SG illumination dataset, the proposed method achieves competitive results in both quantitative metrics and visual quality, outperforming state-of-the-art methods.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102222"},"PeriodicalIF":5.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis 增强型 UrduAspectNet：利用双峰注意力实现卓越的基于方面的情感分析

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-23 DOI: 10.1016/j.jksuci.2024.102221

Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji

{"title":"Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis","authors":"Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji","doi":"10.1016/j.jksuci.2024.102221","DOIUrl":"10.1016/j.jksuci.2024.102221","url":null,"abstract":"<div><div>Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102221"},"PeriodicalIF":5.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Echocardiographic mitral valve segmentation model 超声心动图二尖瓣分割模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-19 DOI: 10.1016/j.jksuci.2024.102218

Chunxia Liu , Shanshan Dong , Feng Xiong , Luqing Wang , Bolun Li , Hongjun Wang

{"title":"Echocardiographic mitral valve segmentation model","authors":"Chunxia Liu , Shanshan Dong , Feng Xiong , Luqing Wang , Bolun Li , Hongjun Wang","doi":"10.1016/j.jksuci.2024.102218","DOIUrl":"10.1016/j.jksuci.2024.102218","url":null,"abstract":"<div><div>Segmentation of mitral valve is not only important for clinical diagnosis, but also has far-reaching impact on prevention and prognosis of the disease by experts and doctors. In this paper, the multi-channel cross fusion transformer based U-Net network model (MCCT-UNet) is proposed according to the classical U-Net architecture. First, the jump connection part of MCCT-UNet is designed by using a multi-channel cross-fusion based attention mechanism module (MCCT) instead of the original jump connection, and this module fuses the feature maps from different scales in different stages of the encoder. Second, the optimization of the feature fusion method is proposed in the decoding stage by designing the cross-compression excitation sub-module (C-SENet) to replace the simple feature splicing, and the C-SENet is used to bridge the inconsistency of the semantic hierarchy by effectively combining the deeper information in the encoding stage with the shallower information. This two modules can establish a close connection between the encoder and decoder by exploring multi-scale global contextual information to solve the semantic divide problem, thus it significantly improves the segmentation performance of the network. The experimental results show that the improvement is effective, and the MCCT-UNet model outperforms the other 9 network models. Specifically, the MCCT-UNet achieved a Dice coefficient of 0.8734, an IoU of 0.7854, and an accuracy of 0.9977, demonstrating significant improvements over the compared models.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102218"},"PeriodicalIF":5.2,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm 萤火虫森林无迭代群集智能聚类算法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-18 DOI: 10.1016/j.jksuci.2024.102219

Shijie Zeng , Yuefei Wang , Yukun Wen , Xi Yu , Binxiong Li , Zixu Wang

{"title":"Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm","authors":"Shijie Zeng , Yuefei Wang , Yukun Wen , Xi Yu , Binxiong Li , Zixu Wang","doi":"10.1016/j.jksuci.2024.102219","DOIUrl":"10.1016/j.jksuci.2024.102219","url":null,"abstract":"<div><div>The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm’s superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102219"},"PeriodicalIF":5.2,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection IMOABC：用于高维特征选择的高效多目标滤波器-包装器混合方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-09 DOI: 10.1016/j.jksuci.2024.102205

Jiahao Li , Tao Luo, Baitao Zhang, Min Chen, Jie Zhou

{"title":"IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection","authors":"Jiahao Li , Tao Luo, Baitao Zhang, Min Chen, Jie Zhou","doi":"10.1016/j.jksuci.2024.102205","DOIUrl":"10.1016/j.jksuci.2024.102205","url":null,"abstract":"<div><div>With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102205"},"PeriodicalIF":5.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach 耦合相移 STAR-RIS 网络中的高级安全措施：DRL 方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-09 DOI: 10.1016/j.jksuci.2024.102215

Abdul Wahid , Syed Zain Ul Abideen , Manzoor Ahmed , Wali Ullah Khan , Muhammad Sheraz , Teong Chee Chuah , Ying Loong Lee

{"title":"Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach","authors":"Abdul Wahid , Syed Zain Ul Abideen , Manzoor Ahmed , Wali Ullah Khan , Muhammad Sheraz , Teong Chee Chuah , Ying Loong Lee","doi":"10.1016/j.jksuci.2024.102215","DOIUrl":"10.1016/j.jksuci.2024.102215","url":null,"abstract":"<div><div>The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102215"},"PeriodicalIF":5.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Endoscopic video aided identification method for gastric area 内窥镜视频辅助胃区识别方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102208

Xiangwei Zheng, Dejian Su, Xuanchi Chen, Mingzhe Zhang

{"title":"Endoscopic video aided identification method for gastric area","authors":"Xiangwei Zheng, Dejian Su, Xuanchi Chen, Mingzhe Zhang","doi":"10.1016/j.jksuci.2024.102208","DOIUrl":"10.1016/j.jksuci.2024.102208","url":null,"abstract":"<div><div>Probe-based confocal laser endomicroscopy (pCLE) is a significant diagnostic instrument and is frequently utilized to diagnose the severity of gastric intestinal metaplasia (GIM). The physicians must comprehensively analyze video clips recorded with pCLE from the gastric antrum, gastric body, and gastric angle area to determine the patient’s condition. However, due to the limitations of the pCLE’s microscopic imaging structure, the gastric areas detected cannot be identified and recorded in real time, which may poses a risk of missing potential areas of disease occurrence and is not conducive to the subsequent precise treatment of the lesion area. Therefore, this paper proposes an endoscopic video aided identification method for identifying gastric areas (EVIGA), which are utilized for determining the detected areas of pCLE in real-time. Firstly, the start time of the diagnosis clip is determined by real-time detecting the working states of pCLE. Then, the endoscopic video clip is truncated according to the correspondence between pCLE and endoscopic video in the time sequence for detecting gastric areas. In order to accurately identify pCLE detected gastric areas, a probe-based confocal laser endomicroscopy diagnosis area identification model (pCLEDAM) is constructed with an hourglass convolution designed for single-frame feature extraction and a temporal feature-sensitive extraction structure for spatial feature extraction. The extracted feature maps are unfolded and fed into the fully connected layer to classify the detected areas. To validate the proposed method, 67 clinical confocal laser endomicroscopy diagnosis cases are collected from a tertiary care hospital, and 500 video clips are finally reserved after audited for dataset construction. Experiments show that the accuracy of area identification on the test dataset achieves 96.0% and is much higher than other related algorithms, achieving the accurate identification of pCLE detected areas.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102208"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On-chain zero-knowledge machine learning: An overview and comparison 链上零知识机器学习：概述与比较

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102207

Vid Keršič, Sašo Karakatič, Muhamed Turkanović

{"title":"On-chain zero-knowledge machine learning: An overview and comparison","authors":"Vid Keršič, Sašo Karakatič, Muhamed Turkanović","doi":"10.1016/j.jksuci.2024.102207","DOIUrl":"10.1016/j.jksuci.2024.102207","url":null,"abstract":"<div><div>Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102207"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPSRM: An intent perceived sequential recommendation model IPSRM：意图感知顺序推荐模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102206

Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan

{"title":"IPSRM: An intent perceived sequential recommendation model","authors":"Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan","doi":"10.1016/j.jksuci.2024.102206","DOIUrl":"10.1016/j.jksuci.2024.102206","url":null,"abstract":"<div><h3>Objectives:</h3><div>Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process.</div></div><div><h3>Methods:</h3><div>This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process.</div></div><div><h3>Results:</h3><div>Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions.</div></div><div><h3>Conclusions:</h3><div>Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102206"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring 用于肿瘤生物标记物评分的全切片图像实时分割和分类

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102204

Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan

{"title":"Real-time segmentation and classification of whole-slide images for tumor biomarker scoring","authors":"Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan","doi":"10.1016/j.jksuci.2024.102204","DOIUrl":"10.1016/j.jksuci.2024.102204","url":null,"abstract":"<div><div>Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102204"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0