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A DEEP LEARNING APPROACH TO IDENTIFY MRNA LOCALIZATION PATTERNS 16

A DEEP LEARNING APPROACH TO IDENTIFY MRNA LOCALIZATION PATTE...

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16th ieee International symposium on Biomedical Imaging (ISBI)

作者： Dubois, Remy Imbert, Arthur Samacoits, Aubin Peter, Marion Bertrand, Edouard Muller, Florian Walter, Thomas PSL Res Univ Mines ParisTech CBIO Ctr Computat Biol Paris France PSL Res Univ Inst Curie Paris France INSERM U900 Paris France Univ Bristol Sch Cellular & Mol Med Carazo Salas Lab Bristol Avon England Univ Montpellier CNRS Inst Genet Mol Montpellier Montpellier France Inst Pasteur Unite Imagerie & Modelisat Paris France CNRS UMR 3691 Paris France

ISBN: (纸本)9781538636411

The localization of messenger RNA (mRNA) molecules inside cells play an important role for the local control of gene expression. However, the localization patterns of many mRNAs remain unknown and poorly understood. Single Molecule Fluorescence in Situ Hybridization (smFISH) allows for the visualization of individual mRNA molecules in cells. This method is now scalable and can be applied in High Content Screening (HCS) mode. Here, we propose a computational workflow based on deep convolutional neural networks trained on simulated data to identify different localization patterns from large-scale smFISH data.

关键词： RNA localization Spatial Transcriptomics Gene expression High Content Screening Deep Learning image simulation

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Rapid Estimation of Orthogonal Matching Pursuit Representation

Rapid Estimation of Orthogonal Matching Pursuit Representati...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ayan Chatterjee Peter W. T. Yuen Centre for Electronic Warfare Information and Cyber (CEWIC) Cranfield Defence and Security Cranfield University Defence Academy of the United Kingdom Shrivenham UK

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among `S' number of samples. The proposed rapid OMP (r-OMP) runs the loop for `N' atoms, simultaneously estimating for all samples, and, in a real scene since N ≪ S, the proposed approach speeds up OMP by several orders of magnitude. Experiment on a real scene with a popular dictionary learning algorithm, K-SVD, show that the proposed r-OMP completes K-SVD in ≈4% of the computational time compared to using OMP.

关键词： Matching pursuit algorithms Dictionaries signal processing algorithms Correlation Atomic measurements Sparse matrices Machine learning

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SEGMENTING THE LEFT VENTRICLE IN CARDIAC IN CARDIAC MRI: FROM HANDCRAFTED TO DEEP REGION BASED DESCRIPTORS 16

SEGMENTING THE LEFT VENTRICLE IN CARDIAC IN CARDIAC MRI: FRO...

引用

16th ieee International symposium on Biomedical Imaging (ISBI)

作者： Medley, Daniela O. Santiago, Carlos Nascimento, Jacinto C. Inst Super Tecn Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9781538636411

Achieving robust object segmentations requires the ability to discard outliers (invalid observations) in the segmentation process. When considering an object model, such as an active shape model (ASM), described by a set of points, a one-to-one mapping from one model point to one valid observation would be ideal. However, in general, a one-to-many mapping is necessary to ensure the valid observation is detected and, thus, to have a reliable and robust fitting process. In this work, we compare three observation detectors: two of them based on handcrafted texture features and another based on a deep CNN classifier. Furthermore, to reduce from many detections to just one valid observation, we incorporate the Generalized Expectation-Maximization (GEM) algorithm in the ASM framework. This algorithm is able to neglect outliers by assigning them low weights. The proposed methodology exhibits remarkable accuracy with all the detectors in the context of the segmentation of the left ventricle in two publicly available MRI datasets, for which the proposed approach is competitive with other state-of-the-art methods.

关键词： Shape computational modeling image segmentation Feature extraction Active shape model Reliability Strain"

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TOPOLOGY-PRESERVING SHAPE-BASED REGRESSION OF RETINAL LAYERS IN OCT image DATA USING CONVOLUTIONAL NEURAL NETWORKS 16

TOPOLOGY-PRESERVING SHAPE-BASED REGRESSION OF RETINAL LAYERS...

引用

16th ieee International symposium on Biomedical Imaging (ISBI)

作者： Kepp, Timo Ehrhardt, Jan Heinrich, Mattias P. Huettmann, Gereon Handels, Heinz Univ Lubeck Inst Med Informat Lubeck Germany Univ Lubeck Grad Sch Comp Med & Life Sci Lubeck Germany Univ Lubeck Inst Biomed Opt Lubeck Germany

ISBN: (纸本)9781538636411

Optical coherence tomography (OCT) is a non-invasive imaging modality that provides cross-sectional 3D images of biological tissue. Especially in ophthalmology OCT is used for the diagnosis of various eye diseases. Automatic retinal layer segmentation algorithms, which are increasingly based on deep learning techniques, can support diagnostics. However, topology properties, such as the order of retinal layers, are often not considered. In our work, we present an automatic segmentation approach based on shape regression using convolutional neural networks (CNNs). Here, shapes are represented by signed distance maps (SDMs) that assign the distance to the next object contour to each pixel. Thus, spatial regularization is introduced and plausible segmentations can be produced. Our method is evaluated on a public OCT dataset and is compared with two classification-based approaches. The results show that our method has fewer out-liers with comparable segmentation performance. In addition, it has an improved topology preservation, which saves further post-processing.

关键词： image segmentation regression topology OCT retina

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FPGA implementation of an algorithm that enables color constancy

FPGA implementation of an algorithm that enables color const...

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ieee/SICE International symposium on System Integration

作者： Kento Misaka Hirotsugu Okuno Graduate school of Information Science and Technology Osaka Institute of Technology 1-79-1 Kitayama Hirakata-City Osaka Japan Faculty of Information Science and Technology Osaka Institute of Technology 1-79-1 Kitayama Hirakata-City Osaka Japan

ISBN: (数字)9781728166674

ISBN: (纸本)9781728166681

We designed a digital circuit that converts raw image sensor data into illumination-independent color information, and implemented it on a field-programmable gate array (FPGA). The coding algorithm used here is based on the center / surround retinex model, which is inspired by color perception of humans and by the visual signal processing in the visual nervous system. The designed digital circuit contains the following functions: integration of multiple exposure images, logarithmic compression, and difference-of-Gaussian spatial filtering. We built an image sensor system by interfacing a CMOS image sensor to the FPGA. We examined the performance of the system by presenting Mondrian to the image sensor under varying color of illumination. As a result, the system successfully reduced the influence of the color of the illumination.

关键词： image color analysis Lighting image coding image sensors Field programmable gate arrays computational modeling Dogs

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Analyzing Deep Learning Model Inferences for image Classification using OpenVINO

Analyzing Deep Learning Model Inferences for Image Classific...

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ieee International symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： Zheming Jin Hal Finkel Leadership Computing Facility Argonne National Laboratory 7300 S. Cass Ave.Lemont IL USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

It may be desirable to execute deep learning model inferences on an integrated GPU at the edge. While such GPUs are much less powerful than discrete GPUs, it is able to deliver higher floating-point operations per second than a CPU located on the same die. For edge devices, the benefit of moving to lower precision with minimal loss of accuracy to obtain higher performance is also attractive. Hence, we chose 14 deep learning models for image classification to evaluate their inference performance with the OpenVINO toolkit. Then, we analyzed the implementation of the fastest inference model of all the models. The experimental results are promising. Compared to the performance of full-precision (FP32) models, the speedup of the 8-bit (INT8) quantization ranges from 1.02 to 1.56 on an Intel ® Xeon ® 4-core CPU, and the speedup of the FP16 models ranges from 1.1 to 2 on an Intel ® Iris TM Pro GPU. For the FP32 models, the GPU is on average 1.5X faster than the CPU.

关键词： Graphics processing units computational modeling Central processing Unit Kernel Convolution Machine learning Quantization (signal)

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INK REMOVAL FROM HISTOPATHOLOGY WHOLE SLIDE imageS BY COMBINING CLASSIFICATION, DETECTION AND image GENERATION MODELS 16

INK REMOVAL FROM HISTOPATHOLOGY WHOLE SLIDE IMAGES BY COMBIN...

引用

16th ieee International symposium on Biomedical Imaging (ISBI)

作者： Ali, Sharib Alham, Nasullah Khalid Verrill, Clare Rinscher, Jens Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford England Univ Oxford John Radcliffe Hosp Nuffield Dept Surg Sci Oxford England Univ Oxford John Radcliffe Hosp Oxford NIHR Biomed Res Ctr BRC Oxford England

ISBN: (纸本)9781538636411

Histopathology slides are routinely marked by pathologists using permanent ink markers that should not be removed as they form part of the medical record. Often tumour regions are marked up for the purpose of highlighting features or other downstream processing such an gene sequencing. Once digitised there is no established method for removing this information from the whole slide images limiting its usability in research and study. Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner. We propose an efficient pipeline using convolution neural networks that results in ink-free images without compromising information and image resolution. Our pipeline includes a sequential classical convolution neural network for accurate classification of contaminated image tiles, a fast region detector and a domain adaptive cycle consistent adversarial generative model for restoration of foreground pixels. Both quantitative and qualitative results on four different whole slide images show that our approach yields visually coherent ink-free whole slide images.

关键词： Digital histopathology Marker ink removal Deep learning CNN GANs

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TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology images using Local Texture, Appearance, and Gradient Features 3

TAG: Nucleus Detection in Colorectal Adenocarcinomas Histolo...

引用

3rd International Conference on Imaging, signal processing and Communication (ICISPC)

作者： Domoguen, Jansen Keith L. Suarez, Jessie James P. Naval, Prospero C., Jr. Univ Philippines Comp Vis & Machine Intelligence Grp Diliman 1 Quezon City Philippines

ISBN: (纸本)9781728136639

This work considers the problem of detecting nuclei in H&E stained images. The high intra-class and inter-image variability of nuclei of the images itself calls for a more robust method that is able to handle this high variability. Recently, deep learning has become the most popular method in computer vision with many systems achieving state-of-the-art results for a wide array tasks. However, as a trade-off, deep learning methods require tons of data and computational resources that some may not posses. Thus, this work proposes a traditional computer vision pipeline along with TAG, a local feature-based approach that combines texture, appearance, and gradient features which is used in the task of pixel-per-pixel prediction in the virtue of semantic segmentation. Based from the results, the method was shown to be effective despite the challenges of high variability in the dataset.

关键词： nucleus detection nuclei detection histology image texture appearance gradient multi-layer perceptron

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Planar Lens for GHz Fourier Ultrasonics

Planar Lens for GHz Fourier Ultrasonics

引用

ieee International Ultrasonics symposium

作者： Juneho Hwang Benyamin Davaji Justin Kuo Amit Lal SonicMEMS Laboratory School of Electrical and Computer Engineering Cornell University Ithaca NY USA

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154497

The Fourier transform is a widely used mathematical algorithm that decomposes a time-domain function into its component frequencies. A highly efficient ultraSonic Fast Fourier Transform (SFFT) [1] hardware accelerator will drastically improve the computational speed needed in real-time image recognition and signal processing. The planar GHz frequency compatible ultrasonic lens presented in this paper is necessary to realize a compact (<;1cc) SFFT system [2]. The wavefronts generated from an array of piezoelectric transducers are focused by the lens to meet the requirement of wavefront FFT computing. This paper presents a planar GHz ultrasonic lens consisting of SiO_2 posts embedded into a silicon substrate, fabricated using DRIE processing of silicon wafers. The spatial density of the posts generates a varying phase shift for waves passing through the lens, as shown by FEM simulations. The FEM simulations and the devices provide a pathway to verify lens operation experimentally.

关键词： Semiconductor device modeling Time-frequency analysis computational modeling Acoustics Silicon Finite element analysis Lenses

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Mini-Batch VLAD for Visual Place Retrieval 30

Mini-Batch VLAD for Visual Place Retrieval

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30th Irish signals and Systems Conference (ISSC)

作者： Aljuaidi, Reem Su, Jing Dahyot, Rozenn Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland

ISBN: (纸本)9781728128009

This study investigates the visual place retrieval of an image query using a geotagged image dataset. Vector of Locally Aggregated Descriptors (VLAD) is one of the local features that can be used for image place recognition. VLAD describes an image by the difference of its local feature descriptors from an already computed codebook. Generally, a visual codebook is generated from k-means clustering of the descriptors. However, the dimensionality of visual features is not trivial and the computational load of sample distances in a large image dataset is challenging. In order to design an accurate image retrieval method with affordable computation expenses, we propose to use the mini-batch k-means clustering to compute VLAD descriptor(MB-VLAD). The proposed MB-VLAD technique shows advantage in retrieval accuracy in comparison with the state of the art techniques.

关键词： feature extraction content-based image retrieval image processing

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