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mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network 24th

mfTrans-Net: Quantitative Measurement of Hepatocellular Carc...

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international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhao, Jianfeng Xiao, Xiaojiao Li, Dengwang Chong, Jaron Kassam, Zahra Chen, Bo Li, Shuo Western Univ London ON Canada Taiyuan Univ Technol Taiyuan 030000 Shanxi Peoples R China Shandong Normal Univ Sch Phys & Elect Shandong Inst Ind Technol Hlth Sci & Precis Med Shandong Key Lab Med Phys & Image Proc Jinan 250358 Peoples R China London Hlth Sci Ctr London ON Canada Digital Imaging Grp London London ON Canada

ISBN: (纸本)9783030872403;9783030872397

Quantitative measurement of hepatocellular carcinoma (HCC) on multi-phase contrast-enhanced magnetic resonance imaging (CEMRI) is one of the key processes for HCC treatment and prognosis. However, direct automated quantitative measurement using the CNN-based network a still challenging task due to: (1) the lack of ability for capturing long-range dependencies of multi-anatomy in the whole medical image;(2) the lack of mechanism for fusing and selecting multi-phase CEMRI information. In this study, we propose a multi-function Transformer regression network (mfTrans-Net) for HCC quantitative measurement. Specifically, we first design three parallel CNN-based encoders for multi-phase CEMRI feature extraction and dimension reducing. Next, the non-local Transformer makes our mfTrans-Net self-attention for capturing the long-range dependencies of multi-anatomy. At the same time, a phase-aware Transformer captures the relevance between multi-phase CEMRI for multi-phase CEMRI information fusion and selection. Lastly, we proposed a multi-level training strategy, which enables an enhanced loss function to improve the quantification task. the mfTrans-Net is validated on multi-phase CEMRI of 138 HCC subjects. Our mfTrans-Net achieves high performance of multi-index quantification that the mean absolute error of center point, max-diameter, circumference, and area is down to 2.35 mm, 2 38 mm, 8.28 mm, and 116.15 mm(2), respectively. the results show that mfTrans-Net has great potential for small lesions quantification in medical images and clinical application value.

关键词： Quantitative measurement Non-local transformer Phase-aware transformer

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Abnormality Detection in Brain Tissue: A deep Learning Approach

Abnormality Detection in Brain Tissue: A deep Learning Appro...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Kiran Preet Bedi Jitendra Singh Jadon Department of Artificial Intelligence Amity School of Engineering and Technology and Technology Amity University Noida Uttar Pradesh India Department of Artificial Intelligence Amity School of Engineering Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Glioblastoma, the most prevalent and aggressive form of brain cancer in adults, presents substantial challenges in both diagnosis and treatment. A crucial prognostic factor and an indicator of chemotherapy responsiveness in this context is the identification of MGMT promoter methylation within the tumour. However, the existing genetic analysis procedures for brain cancer involve invasive methods and time-intensive processes. the primary aim of this initiative is to enhance diagnosis and treatment strategies for glioblastoma patients, with a focus on reducing the reliance on invasive procedures and streamlining the genetic analysis workflow. through the utilization of radio genomics, the goal is to develop a non-invasive approach that predicts the genetic profile of the tumour exclusively through imaging to tackle these challenges, the Radiological Society of North America (RSNA) and the medical image computing and computer assisted intervention Society (MICCAI Society) have collaborated to host a competition focused on glioblastoma diagnosis and treatment planning. the competition revolves around the use of MRI scans to construct a model capable of accurately forecasting the genetic subtype of glioblastoma by identifying the presence of MGMT promoter methylation. the positive outcomes derived from this competition are poised to significantly contribute to less invasive diagnostic procedures and more personalized treatment strategies for individuals battling brain cancer. this abstract provides a comprehensive overview of the project’s objectives, the contextual backdrop of the competition, and the potential transformative impact on the management and survival rates of those affected by glioblastoma.

关键词： Deep learning image segmentation Magnetic resonance imaging Computational modeling Brain cancer Streaming media Predictive models Brain modeling Planning Tumors

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Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine

Blood Cancer Detection and Classification using Auto Encoder...

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international conference on Communication and Electronics Systems (ICCES)

作者： N. Kanimozhi Swikruti Nayak Keshav Kumar K Vinda Manjramkar Ranjeet Kumar D. Suganthi Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering JECRC University Jaipur Rajasthan India Department of Humanities and Mathematics G. Narayanamma Institute of Technology and Science (for Women) Shaikpet Hyderabad Telangana India Zoology B.N. Bandodkar College of Science (Autonomous) Thane Maharashtra India Department of Electronics & Communication University College of Engineering & Technology Vinoba Bhave University Hazaribagh Jharkhand India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai

Leukaemia is considered as the most common type of blood cancer. the length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. the proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%.

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Continuous-Time Deep Glioma Growth Models 24th

Continuous-Time Deep Glioma Growth Models

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international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Petersen, Jens Isensee, Fabian Koehler, Gregor Jaeger, Paul F. Zimmerer, David Neuberger, Ulf Wick, Wolfgang Debus, Jurgen Heiland, Sabine Bendszus, Martin Vollmuth, Philipp Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Canc Res Ctr Div Med Image Comp HIP Appl Comp Vis Lab Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Hosp Dept Neuroradiol Heidelberg Germany Heidelberg Univ Hosp Neurol Clin Heidelberg Germany German Canc Res Ctr DKTK CCU Neurooncol Heidelberg Germany Heidelberg Inst Radiat Oncol HIRO Div Mol & Translat Radiat Oncol Heidelberg Germany Heidelberg Univ Hosp Heidelberg Ion Beam Therapy Ctr HIT Heidelberg Germany German Canc Res Ctr Clin Cooperat Unit Radiat Oncol Heidelberg Germany

ISBN: (纸本)9783030871994;9783030871987

the ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. the result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models.

关键词： Glioma growth Stochastic time series Generative modeling

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Implementation of Vision Transformer Method in Detecting Brain Tumor Based on medical image

Implementation of Vision Transformer Method in Detecting Bra...

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international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Radli Maulana Arief Isman Kurniawan School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

A brain tumor is one of the most fatal and dangerous diseases that can affect both adults and children. It is an abnormal growth of cells in the nervous system that restricts the brain's normal function. Detection of brain tumors is generally carried out using CT scans or MRI of the head. While MRIs provide excellent views of soft tissues, they can take up to an hour and can be a significant financial burden. therefore, one alternative to detect brain tumors is to use image-based Deep Learning MRI. A model for classifying images that makes use of the Transformer architecture is called the Vision Transformer. It divides an image into small patches, flattens them, embeds them into a D-dimensional space, adds a class token and positional embeddings, and processes them using the Transformer for classification. the aim of this study is to build a model to detect brain tumors using the Vision Transformer method based on a MATLAB data format dataset of 1050 T1-weighted contrast-enhanced MRI images. the model has achieved its best results with an accuracy of 0.93 and an F1 score of 0.93 for the test data.

关键词： Training computer vision Accuracy Magnetic resonance imaging Transformers Brain modeling Mathematical models Data models Tuning Biomedical imaging

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A novel digital image processing based mechanism for liver tumor diagnosis 8

A novel digital image processing based mechanism for liver t...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Sharma, Meenu Parveen, Rafat Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9789380544434

the liver is the most significant internal organ in the human body's abdomen. A person cannot survive without a healthy liver. Liver cancer is a life-threatening illness, difficult to detect by biomedical engineering technicians. Hepatocellular carcinoma (HCC) is the most common type of liver cancer which makes up 75% of cases. Plenty of people with liver tumors have lost their lives because of poor and late detection. Hence it is far essential to discover the tumor at an early stage. So, the principal intention is to detect liver cancer at an earlier stage using the image processing technique. Here the tumors are detected from Magnetic Resonance Imaging images. the image undergoes image pre-processing and is segmented by a hybrid method consisting of the edge and mask method, which is simple and easy to use. Detected tumors are further categorized into the cyst, adenoma, hemangioma, and malignant tumor based on statistical features. the scope of this propounded technique is to highlight and categorized the tumor region present in the Magnetic Resonance Imaging images. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： K-means clustering

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Identification of Tumor using Segmentation & Object Detection: A Comparison and an Analysis

Identification of Tumor using Segmentation & Object Detectio...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Debabrata Swain Ashwini Ramanuj Vaishvi Shah Manish Kumar Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Department of Electronics and Communication Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Abnormal cells proliferate in the brain to form brain tumors, which frequently cause death. Localising and segmenting brain tumors using MRI is a difficult task in medical analysis, yet it is necessary for many applications. Accurate segmentation of brain tumor images is required for the identification and treatment of patients with these tumors. Even though they achieved promising segmentation results on the BRATS 2020 dataset, several of them need more testing and training time due to their complicated structure. In order to help doctors identify tumors in their early stages with high accuracy, we have proposed a two-step deep learning method for the diagnosis of three different types of tumors: necrotic, edema, enhancing, as well as brains free of tumors, using MRI images. the metrics used for evaluating the performance of these models are intersection over union, recall, and precision. the research indicates the precision of YOLO model to be 0.97 showcasing the accurate tumor detections, while recall and IoU of UNet segmentation to be 0.99 and 0.83 respectively. In this study, we addressed the issue of inaccuracy and latency in current artificial diagnosis systems by proposing a robust method for classifying brain tumors using deep learning techniques.

关键词： YOLO Deep learning image segmentation Analytical models Accuracy Computational modeling Magnetic resonance imaging Malignant tumors Brain modeling medical diagnostic imaging

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the Advancements of Autonomous Robotic Surgery: A Path Towards Fully Automated Procedures

The Advancements of Autonomous Robotic Surgery: A Path Towar...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Shivam Kumar Akancha Khaitan Ankush Lodh Pravin Kumar Samanta Amit Bakshi Bishnu Prasad De School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Electronics Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Autonomous robotic surgery stands at the leading edge of a revolution in healthcare where robots, artificial intelligence, machine learning, and advanced imaging promise to perform many surgical procedures with minimal direct human intervention. the potential of such systems is even in the poorest areas to augment access to professional care, to reduce recovery time, and increase precision. they are minimally invasive technologies which are designed to maximize treatment results with minimum errors. However, fully automated surgery is a challenge that requires adaptive decision-making, sophisticated sensory feedback, and the ability to overcome technical constraints. Adoption is further complicated by changing regulatory frameworks and ethical issues like patient consent and data protection. Economic constraints are caused by high development and implementation expenses. this study examines the potential for autonomous robotic surgery to revolutionize healthcare by reviewing recent research and clinical data. It presents the latest developments, key challenges, and potential future developments and, hence, provides an exhaustive overview of the merits and demerits of this technology in relation to changing the nature of surgical care.

关键词： Economics Ethics Minimally invasive surgery image edge detection Decision making Imaging Data protection medical services Machine learning Robot sensing systems

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Quantizing mobilenet models for classification problem 8

Quantizing mobilenet models for classification problem

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Patel, Rupa Chaware, Anita S. N. D. T. Women's University Mumbai India Anna Leela College of Commerce and Economics Mumbai India

ISBN: (纸本)9789380544434

Mobile technology is evolving very rapidly. they have been integrated into the health care domain to assist health care professionals. the computer vision models are generally designed to be used for image detection and image classification problems. MobileNet is the computer vision model that performs well on the mobile devices but it requires cloud services for processing image information and providing the predictions to the users. this leads to increased latency. Processing image data on mobile devices will make the prediction faster. But mobiles are resource-restricted devices in terms of storage, power and computational speed. So an architecture that is small in size, efficient and has good prediction quality for image classification problems is required. Quantizing pre-trained MobileNet architecture compact the model representation and this reduces both the computational cost as well as memory requirement. In this paper quantization aware training approach is used for quantizing MobileNets architecture. Both quantized MobileNetvl and quantized MobileNetv2 models use integer computation only as against the original floating-point baseline models. this reduces the size of models and computational speed with the minimum difference in the accuracy. they are trained for classifying retinal fundus images according to the severity of diabetic retinopathy. From the experimental results, it is observed that the model size is compressed. the testing accuracy for MobileNetvl and MobileNetv2 model is 94% and 96% respectively while quantized MobileNetvl and quantized MobileNetv2 accuracy are observed to be 76% and 77% respectively. this novel approach can be used for classifying any medical image data on mobile devices. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： image classification

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Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases 16th

Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate a...

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16th international conference on Brain Informatics, BI 2023

作者： Shaffi, Noushath Vimbi, Viswan Mahmud, Mufti Subramanian, Karthikeyan Hajamohideen, Faizal College of Computing and Information Sciences University of Technology and Applied Sciences-Sohar OM 311 Sohar Oman Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031430749

Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). the proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. the method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. the hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. the source code for this work can be accessed at https://***/snoushath/***. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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