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Deep Multi-Instance Learning with Induced Self-Attention for Medical Image Classification

Deep Multi-Instance Learning with Induced Self-Attention for...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zhenliang Li Liming Yuan Haixia Xu Rui Cheng Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

关键词： Feature extraction Medical diagnostic imaging Cancer Breast Logic gates Task analysis Standards

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Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data

Leveraging Vision Transformers for Enhanced Accuracy in Pneu...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Kanishka Ranaweera Pubudu N. Pathirana School of Engineering Deakin University Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Medical image analysis has witnessed a paradigm shift with the advent of artificial intelligence, particularly the application of Vision Transformers (ViTs). In this study, we leverage the unique attention mechanisms of ViTs to enhance the representation learning of pneumonia-related patterns in medical images. We propose a novel framework that integrates ViTs into the diagnostic pipeline, aiming to capitalize on their ability to capture long-range dependencies and contextual information. The Vision Transformer’s unique ability to capture intricate patterns within radiological images illuminates previously unseen insights, establishing it as a promising tool for revolutionizing pneumonia diagnosis in clinical settings. The experimental evaluation conducted on a comprehensive dataset demonstrates superior performance compared to traditional approaches. The results highlight the potential of ViTs as a valuable tool in advancing the accuracy of pneumonia detection from medical imaging data, contributing to the ongoing efforts in improving diagnostic capabilities for respiratory diseases.

关键词： Representation learning Computer vision Pneumonia Accuracy Image analysis computational modeling Transformers Medical diagnostic imaging X-ray imaging Residual neural networks

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A convex gradient coil design for vertical field open MRI

A convex gradient coil design for vertical field open MRI

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： S.Y. Lee M.H. Cho C.H. Moon H.W. Park Department of Medical Engineering Graduate School of East-West Medicine Kyung Hee University South Korea Department of Medical Engineering Graduate School of East-West Medicine Kyung Hee University South Korea Department of Electrical Engineering KAIST South Korea

ISBN: (纸本)0780364651

We introduce a convex gradient coil that can be used for interventional studies using a vertical field open MRI system. By placing gradient coil wires on two coaxial convex surfaces seeing each other, we can improve the accessibility to the patient during the scan, while maintaining the space for imaging. We have defined convex surfaces on hyperboloids at the prolate spheroidal coordinate. If we take account of accessibility at the figure of merit for the gradient coil performance, the convex gradient coil has better performance than conventional planar gradient coils.

关键词： Magnetic resonance imaging Coils Current density Magnetic fields Cost function Wires Coaxial components Design methodology Equations computational intelligence Society

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Exploration of the Possibility of Early Diagnosis for Digestive Diseases Using Deep Learning Techniques

Exploration of the Possibility of Early Diagnosis for Digest...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Rory Liao Kevin Qi Daniel Che Tingying Helen Zeng Acton Boxborough Regional High School Acton MA USA Boston College Chestnut Hill MA U.S.A Academy for Advanced Research and Development 14th floor One Broadway Cambridge MA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Digestive Diseases are a commonality among Americans (almost one out of every five Americans are affected by digestive diseases). The most effective way of identifying such diseases is through Endoscopic Examinations. While patients normally have to wait for a long time to take high quality images from Endoscopic Examinations for diagnosis. This waiting period is problematic because some of these diseases can turn cancerous. This brings in the need for quick and accurate diagnosis of digestive diseases because the ability to identify diseases and the severity of the diseases vary from one doctor to another. This study analyzes the ability for our deep learning models to distinguish between the Z-Line, the Pylorus, the Cecum, Esophagitis, Ulcerative Colitis, Polyps, Dyed-Lifted Polyps and dyed resurrection margins. We developed a Convolutional Neural Network algorithm to distinguish between the anatomical landmarks to reach pathological findings. We also experimented with three transfer learning models to compare the final results, and ended up with 85 percent accuracy primarily with research optimization of the AlexNet type model. This indicates a significant impact of our artificial intelligence technology on making tremendous strides to be a tool that doctors may use in the near future, so as to improve the overall health of humanity.

关键词： Medical services Intestines Cancer computational modeling Stomach Predictive models Esophagus

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Automatic Fracture Image Recognition Based on an Improved Multi-Stream Residual Convolutional Neural Network

Automatic Fracture Image Recognition Based on an Improved Mu...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yutong Xia Dawei Qiu Cheng Zhang Liang Jiang Jing Liu School of Medical Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This study proposes an automatic fracture image recognition method based on an improved Multi-Stream Residual Convolutional Neural Network (MSR-CNN). Fractures, typically caused by external forces or diseases, are often accompanied by severe pain, swelling, and functional impairment. This is particularly concerning for the elderly, as fractures can lead to prolonged bed rest and multiple complications, significantly affecting their quality of life. Imaging techniques such as Xray, CT, MRI, and ultrasound play a crucial role in fracture diagnosis. However, traditional diagnostic methods heavily rely on the subjective judgment of radiologists, which has inherent limitations. The application of computer-aided diagnosis (CAD) and artificial intelligence technologies has brought about new breakthroughs in medical image recognition. The proposed MSRCNN model combines multi-stream network structures with residual network modules, effectively enhancing the accuracy and efficiency of fracture image recognition. Experimental results demonstrate that the model achieves excellent performance in terms of accuracy, precision, recall, and F1 scores in fracture detection tasks, highlighting its potential for broader applications in medical image analysis.

关键词： Solid modeling Image recognition Accuracy Image analysis computational modeling Magnetic resonance imaging Computed tomography Convolutional neural networks Medical diagnostic imaging Residual neural networks

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Neural and Statistical Classification to Families of Bio-sequences

Neural and Statistical Classification to Families of Bio-seq...

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International Joint conference on Neural Networks (IJCNN)

作者： M. Daoud S.C. Kremer CIS Department University of Guelph Guelph ONT Canada

In this paper we present a novel technique to compute feature vectors for use with artificial neural networks and other pattern recognition techniques that is designed for classifying families of biological sequences. Such sequences present unique challenges due to the fact that they vary in length and often consist of many symbols relative to the number of exemplars available. The latter property presents a specific challenge with respect to avoiding over generalization. We explore a novel approach involving computing the entropy of pair-wise correlations between co-occurring symbols in the strings to generate feature vectors which are of fixed size, much smaller than the original string lengths, and still effective at discerning differences between classes of strings. We apply the technique and show its effectiveness on an RNA family classification problem.

关键词： Biological information theory Pattern recognition RNA Artificial neural networks Data mining biology computing Encoding computational intelligence Society Feature extraction Frequency

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KdINet: Knowledge-driven Interpretable Network for Medical Imaging Diagnosis

KdINet: Knowledge-driven Interpretable Network for Medical I...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yiming Cao Lizhen Cui Lei Zhang Fuqiang Yu Zhen Li Yonghui Xu Chunyan Miao School of Software Shandong University Jinan China Joint SDU-NTU Centre for Artificial Intelligence Research(C-FAIR) Shandong University Jinan China Department of Gastroenterology Qilu Hospital of Shandong University Jinan China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Automatic diagnosis for medical images is a significant research problem of computer-aided diagnosis to reduce the workload of doctors in recent years. However, existing deep learning approaches for diagnoses are usually black-box models with implicit decision-making processes that make them inexplainable. To alleviate the issue, in this paper, we propose a Knowledge-driven Interpretable Network (KdINet) for interpretable disease classification of medical images. KdINet first exploits the pretrained CNN module and the hierarchical representation module to learn two different disease feature representations (i.e., visual disease features and hierarchical disease features). Subsequently, the joint training of two disease features by KdINet’s disease classifier learns a hierarchical classification criterion to infer the diagnosis and generate corresponding interpretable justifications (i.e., ancestral disease paths of the diagnosis). Extensive experiments on three datasets demonstrate that our KdINet achieves significantly higher effectiveness than state-of-the-art approaches on disease classification metrics.

关键词： Training Measurement Deep learning Visualization computational modeling Decision making Medical services

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Evaluation of Frequency-Dependent Effects of Deep Brain Stimulation in a Cortex-Basal Ganglia-Thalamus Network Model of Parkinson’s Disease*

Evaluation of Frequency-Dependent Effects of Deep Brain Stim...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Marcelo R. Romano Renan C. Moioli Leonardo A. Elias Neural Engineering Research Laboratory School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Graduate Program in Bioinformatics Digital Metropolis Institute Federal University of Rio Grande do Norte Natal Brazil Center for Biomedical Engineering University of Campinas Campinas SP Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing the cortex-basal ganglia-thalamus network. Currently, deep brain stimulation (DBS) is used as an effective therapy for reducing the excessive power and synchrony observed in brain circuits, thereby ameliorating the PD symptoms. In the present study, we used a biologically plausible computational model of cortex-basal ganglia-thalamus network, which represents both healthy and PD conditions, to systematically investigate the effects of DBS frequency on the model outputs. DBS was applied to the subthalamic nucleus (STN) at different stimulation frequencies (40Hz to 300Hz). Spike train variability and spectral power in the 7-35Hz band were measured from the several nuclei represented in the model. In addition, the magnitude squared coherence between the nuclei was assessed. An increased DBS frequency tended to produce interspike intervals (ISIs) with higher variability as compared to PD condition. Also, DBS significantly reduced the alpha-beta power for almost all brain nuclei. The median of the magnitude-squared coherence matrix (which is a metric of global network synchronization) decreased significantly with the increase of DBS frequency.

关键词： Satellite broadcasting Brain modeling Neurons computational modeling Coherence Partial discharges Synchronization

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Diagnosis Neuro-Oncology Diseases via Artificial intelligence and Multi-Sequence MRI Methods

Diagnosis Neuro-Oncology Diseases via Artificial Intelligenc...

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Cognitive Sciences, Genomics and bioinformatics (CSGB)

作者： Andrey Letyagin Evgeniya Amelina Bair Tuchinov Vladimir Groza Mikhail Amelin Sergey Golushko Evgeniy Pavlovskiy Branch of IC&G SB RAS Research Institute of Clinical and Experimental Lymphology Novosibirsk Russia SDAML lab. Novosibirsk State University Novosibirsk Russia Radiodiagnosis dept. FSBI “Federal Neurosurgical Center” Novosibirsk Russia Novosibirsk State University Novosibirsk Russia

The study of brain tumor structure and its type-dependent variations is one of the most important research areas in which medical imaging techniques are used. The structural and statistical analysis of these lesions raises various related problems and projects, such as the detection of the neuro oncology diseases, the shape and the segmentation of specific sub-regions (i.e. necrotic part, (non-)enhanced part, edema), the classification of the tumor occurrence and the subsequent treatment up-prognosis. Almost all of these problems are usually solved numerically, particularly with the tendency to use methods related to artificial intelligence (AI), often including deep learning (DL) networks. One of the most complicated, least researched and challenging tasks in this field is the classification of tumor types. This difficulty can be explained by several reasons, the most important of which is the severe limitation of existing open-source datasets that contain clinically confirmed tumor type designations based on radiological examination protocols. Magnetic resonance imaging (MRI) is the most common method for screening, primary detection and non-invasive diagnosis of brain diseases, as well as a source of recommendations for further treatment and observation. In this paper, we extend the previous research works on the robust multi-sequences segmentation and classification methods which allows to consider all available information from MRI scans by the composition of TI, TIC, T2 and T2-FLAIR sequences. It is based on the clinical radiology hypothesis and presents an efficient approach to combining and matching 3D methods to search for areas of comprised the GD-enhancing tumor in order to significantly improve the model's performance of the particular applied numerical problem of brain tumor classification and metastasis segmentation. All investigations performed and results presented are based on the private Siberian brain tumor dataset, including labeled volumetric MR

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Automatic target localization using microelectrode recordings

Automatic target localization using microelectrode recording...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： R.A. Santiago J. McNames H. Falkenberg K. Burchiel Northwest Computational Intelligence Laboratory Portland State University OR USA Biomedical Signal Processing Laboratory Electrical & Computer Engineering Portland State University OR USA Department of Neurological Surgery Oregon Health and Sciences University OR USA

We describe an algorithm that objectively and automatically identifies target regions in the brain for ablation or stimulation during neurosurgery for Parkinson's disease and other movement disorders. The algorithm uses microelectrode recordings to distinguish between the target and adjacent anatomic structures during stereotactic neurosurgery. This algorithm uses a novel method of signal feature extraction that enables standard classification algorithms such as support vector machines to perform well. The algorithm was validated on microelectrode recordings acquired near the globus pallidus internus and labeled by the neurosurgeon.

关键词： Microelectrodes Surgery Signal processing algorithms Neurosurgery Parkinson's disease Magnetic resonance imaging Neurons Laboratories Support vector machines Support vector machine classification

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