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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhongyi Hu Libin Gao Yinsheng Tong Xingjin Lu Rui Li Lei Xiao Zuoyong Li College of Computer Science and Aritificial Intelligence Wenzhou University Wenzhou China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases.

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On the risk of manual annotations in 3D confocal microscopy image segmentation

On the risk of manual annotations in 3D confocal microscopy ...

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international conference on computer Vision Workshops (ICCV Workshops)

作者： Justin Sonneck Shuo Zhao Jianxu Chen Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V.

image segmentation in 3D confocal fluorescence microscopy images is a common problem in many biomedical studies. Deep learning-based methods have achieved great success on such tasks. In the literature, manual 3D annotations are still commonly used for model training or performance evaluation. But, due to the nature of the lens-based optical instruments, diffraction of light always occurs, which can lead to obscure boundaries of the biomedical structures being imaged. For example, when analyzing nuclei from 3D fluorescence microscopy images of cells marked by DNA dyes, the exact boundaries are usually not clearly identifiable, especially along Z. This makes accurate segmentation, both manually and automatically, very challenging. For applications where the boundary accuracy is crucial, the downstream analyses can thus be significantly compromised. This problem can be addressed with special experimental-computational co-design to acquire the "biological ground truth". For the nuclei example, we can take cells expressing mEGFP tagged lamin B1, from which we can acquire both the DNA dye channel (nucleus) and the lamin B1 channel (nuclear envelope). Lamin B1 signals clearly mark the nuclei boundary and can thus serve as the real truth. We demonstrate that training a deep learning-based nuclei instance segmentation model with biological ground truth and manual annotations will result in significant differences in various metrics, such as volume or application-specific measurements. Also, we show the universalness of such issues with manual annotations by testing different state-of-the-art deep learning-based methods. We hope our work can raise within the biomedical image analysis community the awareness of (1) the importance of interdisciplinary collaborations, e.g., computational-experimental co-design for biological ground truth collection, and (2) potentially significant issues with manual annotation in training or evaluating deep learning-based segmentation models.

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Medical image Fusion Based on Adaptive Pulse Coupled Neural Networks and Nonsubsampled Contourlet Transform

Medical Image Fusion Based on Adaptive Pulse Coupled Neural ...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Rushan Chen Mengxing Huang Yu Zhang Yuanyuan Wu State Key Laboratory of Marine Resource Utilization in South China Sea Resource College of Information Science and Technology Hainan University Haikou China College of Computer Science and Technology Hainan University Haikou China

Multi-modal medical image fusion can effectively integrate imaging information from multiple sensors and has become a research hotspot in medical image processing in recent years. In this paper, a new multimodal medical image fusion method based on adaptive pulse-coupled neural network (PCNN) and nonsubsampled contour wave transform (NSCT) is proposed. In the proposed, the NSCT decomposition is first performed on medical source images to obtain high-frequency subbands and low-frequency subbands. Secondly, the fusion strategy of phase coherence model for feature extraction of low frequency sub-bands can effectively retain the energy information of the source image. In addition, a new high-frequency subband fusion rule is designed to customize the decomposed high-frequency sublayer, and further optimized for the different frequency band characteristics of the decomposed obtained sublayer. And a new input parameter adaption is used to improve the PCNN model as a strategy for high frequency coefficient fusion. Finally,the fused image is reconstructed by performing inverse NSCT on the fused high-frequency and low-frequency bands. Experiments are conducted by performing on a publicly accessible brain image dataset. The experimental results show that the algorithm achieves positive performance in terms of visual quality and the objective indicators of overall Cross entropy (OCE), structural similarity (SSIM), feature mutual information (FMI) and visual fidelity (VIFF) have reached the best level.

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Building and Validating a Clinical Ultrasound image Reporting Model

Building and Validating a Clinical Ultrasound Image Reportin...

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ieee international conference on Information Reuse and Integration (IRI)

作者： Meng-Che Tsai Kuo-Chung Chu Yi-Xian Li Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Education and Research Taipei City Hospital Taipei Taiwan

The main job of a radiologist is to understand the essential information hidden in medical images and write diagnostic reports, which are very helpful for subsequent clinical treatment. However, due to the difficulty of interpreting medical images, it requires long-term training. If the training time is short and the experience is insufficient, it will lead to errors in subsequent clinical diagnosis. In addition, the aging population has increased the workload for radiologists, especially with more elderly patients. Therefore, in the case of insufficient workforce and time costs, this study established an Encoder-Decoder architecture for ultrasound image report generation. The Encoder used Faster RCNN to extract lesion-related features from the image, while the Decoder used LSTM to describe the lesion features in words. This approach can effectively assist radiologists in writing diagnostic reports. Faster RCNN and LSTM have shown superior performance in computer vision and natural language processing, but their performance may not be as expected when the dataset is insufficient. Especially, the collection of medical images and reports is difficult, which may result in generated reports that cannot be used. Therefore, this study introduces the idea of prior knowledge, which integrates the lesion organs classified by Faster RCNN and the lesion image features into LSTM to improve the accuracy of describing the lesion organ names and reduce the model’s description errors of organs in small samples, thus increasing the trust of physicians in the report. Finally, in the experimental results, introducing prior knowledge of lesion organ names has a better effect, and the generated reports all contain organ names related to ultrasound images.

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Noise Optimized Order-Statistic Energy Pre-fitted Active Contour for Accurate image Segmentation

Noise Optimized Order-Statistic Energy Pre-fitted Active Con...

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international conference on computer Theory and applications (ICCTA)

作者： C. M. A. Rahman Satirtha Paul Shyam P. K. Partho Wasi Mashrur Shian Department of Electrical Electronic and Communication Engineering (EECE) Military Institute of Science and Technology (MIST) Dhaka Bangladesh

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

Active contour models (ACM) are best known for their simplicity and tunability in segmenting images having complex backgrounds, random noises, intensity inhomogeneity, and irregular shapes and sizes of region of interest (ROI). These models are capable of effectively using image local and global information to segment objects in multi-modal images without the requirement of large sets of training datasets. To decrease the computational costs of ACMs, pre-fitting intensity information has become a prominent option nowadays. However, pre-fitting terms sometimes increase pre-processing time, decrease accuracy, and may cut down the noise tolerance of the model. To address these issues, this work utilizes non-linear pixel sorting to calculate local intensity descriptors and construct a second-order differential loss function. The loss function is further optimized for noise cancellation and pre-fitted in the variational framework. Therefore, the proposed design attains reduced computational overhead, better accuracy and improved noise endurance compared to recently developed and related ACMs.

关键词： Training image segmentation Accuracy Computational modeling image edge detection Noise Fitting Optimization Active contours Sorting

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Application of image Recognition Algorithms in the Detection of Philippine Lime Diseases

Application of Image Recognition Algorithms in the Detection...

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ieee international conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Mikho J Pelingon Valenzuela Franco Carlos Maria Leonora C Guico Jan Kevin A Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Calamansi has been declared as one of the most important fruit growing crops in the Philippines. However, due to certain bacteria, it is susceptible to certain diseases affecting its harvest rate. This paper aims to effectively monitor the state of the calamansi at its healthy state and at its diseased state. Specifically, it classifies diseases such as Citrus Canker, Citrus Scab, and Citrus Browning by utilizing existing image processing techniques for disease detection of different fruits and determining which algorithm is most apt for this application in terms of precision, accuracy and recall. Techniques such as K-Means Clustering, utilization of an Artificial Neural Network (ANN), feature extraction through GLCM along with the usage of a minimum distance classifier, a Support Vector Machine (SVM) classifier and other techniques and/or their combinations were explored and measured. The researchers performed two kinds of tests: 1×1 comparison and merged comparison. For the 1×1 comparison, making use of GrabCut, color feature extraction, and SVM produced the best overall results, with an overall average of 98% for precision, 95% for accuracy, 91% for recall, and 94% for F-score. Adaptive Gaussian Filtering along with texture feature extraction and SVM was the most accurate for detecting calamansi fruits with citrus canker and citrus scab. Overall, the two methods acquired the same average accuracy of 61%

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An Optimized Ant Gardient Convolutional Neural Network for Disease Detection in Apple Leaves

An Optimized Ant Gardient Convolutional Neural Network for D...

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Innovation in Technology (INOCON), ieee international conference for

作者： Arshleen Kaur Raman Chadha Computer Science and Engineering Chandigarh University Gharuan Mohali (Punjab) India

This article implements a new method of classification or detection of the diseased section in apple leaf images. Apple leaf disease segmentation and classification is the main processing phase for ROI extraction in agriculture-based IP (image processing). Generally, in the case of live images, it is also crucial to separate the apple leaf images from the background. A frequent experiment in processing leaf images is the automated separation of apple leaves from the background. In the research work, ALS (Alternaria leaf spot), BS (brown spot), Mosaic, etc., are five normal categories of ALD (apple leaf diseases) that rigorously affect apple production. Though the previous work lacks accuracy and faster detection of apple diseases to confirm the healthy growth of apple production, it implements a DL method based on the CNN classifier for detecting apple leaf diseases. The OAGCNN detection model is proposed, which combines an ant colony optimizer and stochastic gradient descent method with the CNN model. The simulation outcomes define the OAGCNN model performance of 98.5 per cent on the ALDD (apple leaf disease database), with a maximum detection time of 2 seconds. The outcomes validate that the OA-CNN classifier model gives maximum performance for the early-stage detection of ALLD and may execute real-time detection of these diseases with maximum accuracy rate and detection complexity than existing techniques such as Vgg-Icep and multi-label classifier methods.

关键词： Technological innovation image segmentation Databases Stochastic processes Production Real-time systems Complexity theory

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Freshy Pal: A Proposal of an Interactive Web based Tool to Automatically Detect Microbial Growth on White Bread

Freshy Pal: A Proposal of an Interactive Web based Tool to A...

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international conference for Convergence of Technology (I2CT)

作者： Harshee Pitroda Abhay Kolhe Department of Computer Engineering NMIMS University Mukesh Patel School of Technology Management & Engineering Mumbai India

Bread has long been a staple food in kitchens across the country and is devoured by billions of people worldwide. Despite being so widely consumed, the bread usually only remains fresh for a short while. Bread can develop molds and microbial growth, making it unhealthy to ingest and potentially harmful to one's health. As a result, it is necessary to determine whether the bread is safe to consume. Hence, Freshy Pal, an interactive web-based application tool was created, that makes significant use of new technologies such as image processing, machine learning, and deep learning to identify bread spoilage given an image of the slice of bread. The primary goal of designing this application is to assist users in determining whether bread is safe to ingest because bread spoilage can cause allergic responses and respiratory difficulties, further removing the clearly spoilt portion of the bread and consuming the remainder is equally harmful. So, after Freshy Pal classifies bread as unsafe to consume, it instructs the users on how to properly dispose of and handle the moldy bread. Freshy Pal also educates its users on how to keep their bread safe from mold growth. Two different methodologies were used to develop the system. The first approach employed extensive image processing and Machine Learning methods. The following machine learning algorithms were used: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, and Ada Boost. Random Forest and Ada boost achieved the greatest accuracy of 96.67%. The second approach employed Deep Learning-based image Classification utilizing Convolutional Neural Networks, here an accuracy of 98% was achieved using the pre-trained model VGG16. The outcomes of both techniques are contrasted on a variety of grounds. The deep learning-based method achieved higher accuracy, but it required longer training time and larger data size.

关键词： Deep learning Training Support vector machines Microorganisms image processing Neural networks Minerals

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Wider is Better? Contact-free Vibration Sensing via Different COTS-RF Technologies

Wider is Better? Contact-free Vibration Sensing via Differen...

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ieee Annual Joint conference: INFOCOM, ieee computer and Communications Societies

作者： Zhe Chen Tianyue Zheng Chao Cai Yue Gao Pengfei Hu Jun Luo AIWiSe China-Singapore International Joint Research Institute China Nanyang Technological University Singapore Huazhong University of Science and Technology China Fudan University China Shandong University China

Vibration sensing is crucial to human life and work, as vibrations indicate the status of their respective sources (e.g., heartbeat to human health condition). Given the inconvenience of contact sensing, both academia and industry have been intensively exploring contact-free vibration sensing, with several major developments leveraging radio-frequency (RF) technologies made very recently. However, a measurement study systematically comparing these options is still missing. In this paper, we choose to evaluate five representative commercial off-the-shelf (COTS) RF technologies with different carrier frequencies, bandwidths, and waveform designs. We first unify the sensing data format and processing pipeline, and also propose a novel metric v-SNR to quantify sensing quality. Then our extensive evaluations start from controlled experiments for benchmarking, followed by investigations on two real-world applications: machinery vibration measurement and vital sign monitoring. Our comprehensive study reveals that Wi-Fi performs the worst among all five technologies, while a lesser-known UWB-based technology achieves the best overall performance, and others have respective pros and cons in different scenarios.

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Self-supervised image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network

Self-supervised Image Denoising with Downsampled Invariance ...

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international conference on computer Vision (ICCV)

作者： Yeong Il Jang Keuntek Lee Gu Yong Park Seyun Kim Nam Ik Cho Department of ECE INMC Seoul National University Seoul Korea Gauss Labs Inc. IPAI Seoul National University Seoul Korea

There have been many image denoisers using deep neural networks, which outperform conventional model-based methods by large margins. Recently, self-supervised methods have attracted attention because constructing a large real noise dataset for supervised training is an enormous burden. The most representative self-supervised denoisers are based on blind-spot networks, which exclude the receptive field’s center pixel. However, excluding any input pixel is abandoning some information, especially when the input pixel at the corresponding output position is excluded. In addition, a standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise, though it successfully removes independently distributed synthetic noise. Hence, to realize a more practical denoiser, we propose a novel self-supervised training framework that can remove real noise. For this, we derive the theoretic upper bound of a supervised loss where the network is guided by the downsampled blinded output. Also, we design a conditional blind-spot network (C-BSN), which selectively controls the blindness of the network to use the center pixel information. Furthermore, we exploit a random subsampler to decorrelate noise spatially, making the C-BSN free of visual artifacts that were often seen in downsample-based methods. Extensive experiments show that the proposed C-BSN achieves state-of-the-art performance on real-world datasets as a self-supervised denoiser and shows qualitatively pleasing results without any post-processing or refinement.

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