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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB Lesion Segmentation and Classification

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： M. Saravana Karthikeyan J. Relin Francis Raj R. Parvathi S. thanga Anushya R. Santhana Krishnan K. Paul Joshua Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India Department of Smart Computing School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. this Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. the attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. this progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis.

关键词： image segmentation Accuracy Attention mechanisms Tuberculosis Feature extraction Boosting Robustness Lesions X-ray imaging Biomedical imaging

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Chinnasamy Ramesh Kumar Ayyasamy K. Anuradha Irfan Alam Deveendra Menon Narayanan Nair Ajmeera Kiran Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of CSE (AI &ML) CVR College of Engineering Telangana India Cyber Security and Digital Forensics Division SCAI VIT Bhopal University Kothari Kalan Sehore India Department of Computer Science and Engineering MLR Institute of Technology Hdyerabad India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of things (IoT) market. this work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. the suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. the private information is embedded within images through the matrix XOR encoding steganography algorithm. the Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. the effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. the results unequivocally validate that our approach offers enhanced safety in Internet of things (IoT) applications.

关键词： Steganography Protocols image coding Elliptic curve cryptography Data transfer Encoding Safety Internet of things Galois fields Optimization

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MR image Super-Resolution Using Wavelet Diffusion for Predicting Alzheimer’s Disease 16th

MR Image Super-Resolution Using Wavelet Diffusion for Predi...

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16th international conference on Brain Informatics, BI 2023

作者： Huang, Guoli Chen, Xuhang Shen, Yanyan Wang, Shuqiang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Macau China

ISBN: (纸本)9783031430749

Alzheimer’s disease (AD) is a neurodegenerative disorder that exerts a substantial influence on individuals worldwide. Magnetic resonance imaging (MRI) can detect and track disease progression. However, the majority of MRI data currently available is characterized by low resolution. the present study introduces a novel approach for MRI super-resolution by integrating diffusion model with wavelet decomposition techniques. the methodology proposed in this study is tailored to address the issue of restricted data availability. It utilizes adversarial training and capitalizes on the advantages of denoising diffusion probabilistic model (DDPM), while simultaneously avoiding the problem of diversity collapse. the proposed method incorporates wavelet decomposition within the latent space to augment the resilience and efficiency of generative models. the experimental findings demonstrate the superior efficacy of our proposed model in contrast to alternative techniques, as indicated by the SSIM and FID metrics. Moreover, our methodology has the potential to enhance the precision of Alzheimer’s disease assessment. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance images: Contribution to the HNTSMRG Challenge 1st

Benchmark of Deep Encoder-Decoder Architectures for Head and...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wodzinski, Marek Department of Measurement and Electronics AGH University of Krakow Krakow Poland Sierre Switzerland

ISBN: (纸本)9783031832734

Radiation therapy is one of the most frequently applied cancer treatments worldwide, especially in the context of head and neck cancer. Today, MRI-guided radiation therapy planning is becoming increasingly popular due to good soft tissue contrast, lack of radiation dose delivered to the patient, and the capability of performing functional imaging. However, MRI-guided radiation therapy requires segmenting of the cancer both before and during radiation therapy. So far, the segmentation was often performed manually by experienced radiologists, however, recent advances in deep learning-based segmentation suggest that it may be possible to perform the segmentation automatically. Nevertheless, the task is arguably more difficult when using MRI compared to e.g. PET-CT because even manual segmentation of head and neck cancer in MRI volumes is challenging and time-consuming. the importance of the problem motivated the researchers to organize the HNTSMRG challenge with the aim of developing the most accurate segmentation methods, both before and during MRI-guided radiation therapy. In this work, we benchmark several different state-of-the-art segmentation architectures to verify whether the recent advances in deep encoder-decoder architectures are impactful for low data regimes and low-contrast tasks like segmenting head and neck cancer in magnetic resonance images. We show that for such cases the traditional residual UNet-based method outperforms (DSC = 0.775/0.701) recent advances such as UNETR (DSC = 0.617/0.657), SwinUNETR (DSC = 0.757/0.700), or SegMamba (DSC = 0.708/0.683). the proposed method (lWM team) achieved a mean aggregated Dice score on the closed test set at the level of 0.771 and 0.707 for the pre- and mid-therapy segmentation tasks, scoring 14th and 6th place, respectively. the results suggest that proper data preparation, objective function, and preprocessing are more influential for the segmentation of head and neck cancer than deep network architecture. ©

关键词： image segmentation

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Head and Neck Tumor Segmentation on MRIs with Fast and Resource-Efficient Staged nnU-Nets 1st

Head and Neck Tumor Segmentation on MRIs with Fast and Resou...

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作者： Tappeiner, Elias Gapp, Christian Welk, Martin Schubert, Rainer UMIT Tirol – Private University for Health Sciences and Health Technology Eduard-Wallnofer-Zentrum 1 Hall in Tirol 6060 Austria

ISBN: (纸本)9783031832734

MRI-guided radiotherapy (RT) planning offers key advantages over conventional CT-based methods, including superior soft tissue contrast and the potential for daily adaptive RT due to the reduction of the radiation burden. In the Head and Neck (HN) region labor-intensive and time-consuming tumor segmentation still limits full utilization of MRI-guided adaptive RT. the HN Tumor Segmentation for MR-Guided Applications 2024 challenge (HNTS-MRG) aims to improve automatic tumor segmentation on MRI images by providing a dataset with reference annotations for the tasks of pre-RT and mid-RT planning. In this work, we present our approach for the HNTS-MRG challenge. Based on the insights of a thorough literature review we implemented a fast and resource-efficient two-stage segmentation method using the nnU-Net architecture with residual encoders as a backbone. In our two-stage approach we use the segmentation results of a first training round to guide the sampling process for a second refinement stage. For the pre-RT task, we achieved competitive results using only the first-stage nnU-Net. For the mid-RT task, we could significantly increase the seg- mentation performance of the basic first stage nnU-Net by utilizing the prior knowledge of the pre-RT plan as an additional input for the second stage refinement network. As team alpinists we achieved an aggregated Dice Coefficient of 80.97 for the pre-RT and 69.84 for the mid-RT task on the online test set of the challenge. Our code and trained model weights for the two-stage nnU-Net approach with residual encoders are available at https://***/elitap/hntsmrg24. © the Author(s) 2025.

关键词： image segmentation

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MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation 39

MoRe: Class Patch Attention Needs Regularization for Weakly ...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center National Supercomputer Center in Jinan China

ISBN: (纸本)157735897X

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the potential of LAM. Our findings suggest that imposing additional regularization on class-patch attention is necessary. To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities. It ensures that class tokens dynamically condense the related patch information and suppress unrelated artifacts at a graph level. Second, motivated by the observation that CAM from classification weights maintains smooth localization of objects, we devise the Localization-informed Regularization module to explicitly regularize the class-patch attention. It directly mines the token relations from CAM and further supervises the consistency between class and patch tokens in a learnable manner. Extensive experiments on PASCAL VOC and MS COCO validate that MoRe effectively addresses the artifact issue and achieves state-of-the-art performance, surpassing recent single-stage and even multi-stage methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Flow graphs

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MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble 1st

MRI-Based Head and Neck Tumor Segmentation Using nnU-Net wit...

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作者： Mol, Frank N. Hoek, Luuk van der Ma, Baoqiang Nagam, Bharath Chowdhary Sijtsema, Nanna M. van Dijk, Lisanne V. Bunte, Kerstin Vlijm, Rifka Van Ooijen, Peter M. A. Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands University of Groningen University Medical Center Groningen Groningen9700 RB Netherlands

ISBN: (纸本)9783031832734

the superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. the Head and Neck Tumor Segmentation for MR-Guided Applications chal- lenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2- weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). the training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopt- ing the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robust- ness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. the mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. these metrics determine the final ranking of methods for both tasks separately. the final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2. © the Author(s) 2025.

关键词： image segmentation

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Retinal image Analysis for Detection of Diabetic Macular Edema Using Transfer Learning

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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conference on Data Science and Machine Learning Applications (CDMA)

作者： Tayyaba Asim Shahzad Akbar Usama Shahzore Amjad Rehman Tanzila Saba Noor Ayesha Riphah College of Computing Riphah International University Faisalabad Pakistan AIDA Lab. CCIS Prince Sultan University Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan RP China

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. the proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets; the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy.

关键词： image segmentation Accuracy Optical coherence tomography Transfer learning computer architecture Retina Diabetes Topology Convolutional neural networks Residual neural networks

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Transfer Learning-assisted DementiaNet: A Four Layer Deep CNN for Accurate Alzheimer’s Disease Detection from MRI images 16th

Transfer Learning-Assisted DementiaNet: A Four Layer Deep CN...

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16th international conference on Brain Informatics, BI 2023

作者： Umme Habiba, Sultana Debnath, Tanoy Islam, Md. Khairul Nahar, Lutfun Shahadat Hossain, Mohammad Basnin, Nanziba Andersson, Karl Khulna University of Engineering & Technology Khulna Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Leeds Beckett University Leeds United Kingdom Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden

ISBN: (纸本)9783031430749

Alzheimer’s disease is the most common type of dementia and the sixth highest cause of mortality among people over 65. Also, according to statistics, the number of deaths from Alzheimer’s disease has increased dramatically. As a result, early detection of Alzheimer’s disease can improve patient survival chances. Machine learning methods using magnetic resonance imaging have been utilized in the diagnosis of Alzheimer’s disease to help clinicians and speed up the procedure. this research mainly focuses on Alzheimer’s disease detection to overcome previous limitations. We use a publicly available dataset which contains 6400 MRI images. In order to train our dataset, we employ our suggested model, "DementiaNet", using "EfficientNet" as a feature extractor and a Deep CNN as a classifier. In order to capture all the features, this framework uses a small number of convolutional layers, which improves the effectiveness of feature learning and results in a more accurate and reliable output. In addition, to address the issue of data imbalance, we apply data augmentation to enhance the size of the minority class by considering four stages of dementia. Also, in this study, we take advantage of the transfer learning approach with the attachment of "EfficientNet", which allows our model to easily solve the overfitting problem and also extract all the features in a effective way. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our "DementiaNet" model achieve an overall classification accuracy of 97% to detect Alzheimer from brain MRI images, exceeding all other state-of-the-art models. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease 16th

Exploring Deep Transfer Learning Ensemble for Improved Diag...

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16th international conference on Brain Informatics, BI 2023

作者： Mahmud, Tanjim Barua, Koushick Barua, Anik Das, Sudhakar Basnin, Nanziba Hossain, Mohammad Shahadat Andersson, Karl Kaiser, M. Shamim Sharmen, Nahed Rangamati Science and Technology University Rangamati4500 Bangladesh Leeds Beckette University Leeds United Kingdom University of Chittagong Chittagong Bangladesh Pervasive and Mobile Computing Laboratory Lulea University of Technology S-931 87 Skelleftea Sweden Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Chittagong Bangladesh

ISBN: (纸本)9783031430749

Alzheimer’s disease (AD) is a progressive and irreversible neurological disorder that affects millions of people worldwide. Early detection and accurate diagnosis of AD are crucial for effective treatment and management of the disease. In this paper, we propose a transfer learning-based approach for the diagnosis of AD using magnetic resonance imaging (MRI) data. Our approach involves extracting relevant features from the MRI data using transfer learning by alter the weights and then using these features to train pre-trained models and combined ensemble classifier. We evaluated our approach on a dataset of MRI scans from patients with AD and healthy controls, achieving an accuracy of 95% for combined ensemble models. Our results demonstrate the potential of transfer learning-based approaches for the early and accurate diagnosis of AD, which could lead to improved patient outcomes and more effective management of the disease. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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