scholarly journals An Enhanced Image Segmentation From 3D to 2D by Using Modified Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1712-1716
Author(s):  
P. Pushpalatha
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography Angiography ◽  
State Of The Art ◽  
Vascular Networks ◽  
Clinical Routine ◽  
Non Invasive ◽  
Previous State ◽  
Visualization Techniques ◽  
High Pass ◽  
Data Acquisition Process

Abstract: Optical coherence tomography angiography (OCTA) is an imaging which can applied in ophthalmology to provide detailed visualization of the perfusion of vascular networks in the eye. compared to previous state of the art dye-based imaging, such as fluorescein angiography. OCTA is non-invasive, time efficient, and it allows for the examination of retinal vascular in 3D. These advantage of the technique combined with the good usability in commercial devices led to a quick adoption of the new modality in the clinical routine. However, the interpretation of OCTA data is not without problems commonly observed image artifacts and the quite involved algorithmic details of OCTA signal construction can make the clinical assessment of OCTA exams challenging. In this paper we describe the technical background of OCTA and discuss the data acquisition process, common image visualization techniques, as well as 3D to 2D projection using high pass filtering, relu function and convolution neural network (CNN) for more accuracy and segmentation results.

scholarly journals From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations

2018 ◽  
Vol 6 ◽  
pp. 343-356 ◽  
Author(s):  
Egoitz Laparra ◽  
Dongfang Xu ◽  
Steven Bethard
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Learning Approaches ◽  
Semantic Parsing ◽  
Time Intervals ◽  
Semantic Composition ◽  
Previous State ◽  
New Scoring

This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.

Progressive leukoencephalopathy caused by primary CNS lymphoma

2010 ◽  
Vol 63 (4) ◽  
pp. 359-361 ◽  
Author(s):  
Benjamin Matošević ◽  
Michael Knoflach ◽  
Martin Furtner ◽  
Thaddäus Gotwald ◽  
Hans Maier ◽  
...  
Keyword(s):  
Small Vessel Disease ◽  
State Of The Art ◽  
Correct Diagnosis ◽  
Primary Cns Lymphoma ◽  
Brain Biopsy ◽  
Cns Lymphoma ◽  
Clinical Routine ◽  
Non Invasive ◽  
Vessel Disease ◽  
Inflammatory Tissue

Prominent leukoaraiosis is common in the clinical routine setting. In addition to microatheroma and hypertensive small vessel disease (lipohyalinosis), a large number of rare but clinically relevant differential diagnoses have to be considered. A man in his 60s presented with left pontine infarction and subsequent rapidly deteriorating leukoaraiosis associated with dementia. Standard non-invasive examination did not enable the correct diagnosis to be obtained. A brain biopsy sample revealed a combination of diffuse infiltrating and intravascular large B cell central nervous system (CNS) lymphoma, which has not previously been described in literature. Despite immediate treatment with state of the art chemotherapy, the patient died 3 months after the onset of symptoms. Diffuse infiltrating and intravascular primary CNS lymphoma is a rare cause of rapidly progressive leukoencephalopathy and stroke mediated by neoplastic microvessel occlusion and inflammatory tissue damage. This report intends to increase awareness among neurologists and other stroke physicians about this disease in order to accelerate diagnosis and initiation of treatment.

scholarly journals MEYE: Web-app for translational and real-time pupillometry

2021 ◽  
Author(s):  
Raffaele Mazziotti ◽  
Fabio Carrara ◽  
Aurelia Viglione ◽  
Leonardo Lupori ◽  
Luca Lo Verde ◽  
...  
Keyword(s):  
Neural Network ◽  
Mouse Models ◽  
State Of The Art ◽  
Web Browser ◽  
Non Invasive ◽  
Temporal Specificity ◽  
On Line ◽  
Web App ◽  
Set Up ◽  
Neuropsychiatric Conditions

AbstractPupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

scholarly journals A Siamese Neural Network for Non-Invasive Baggage Re-Identification

2020 ◽  
Vol 6 (11) ◽  
pp. 126
Author(s):  
Pier Luigi Mazzeo ◽  
Christian Libetta ◽  
Paolo Spagnolo ◽  
Cosimo Distante
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Conveyor Belt ◽  
Handling System ◽  
Non Invasive ◽  
Training Images ◽  
Traffic Jams ◽  
Baggage Handling

Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.

scholarly journals Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alican Bozkurt ◽  
Kivanc Kose ◽  
Jaume Coll-Font ◽  
Christi Alessi-Fox ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
State Of The Art ◽  
Reflectance Confocal Microscopy ◽  
Convolutional Networks ◽  
Non Invasive ◽  
Extensive Training ◽  
Fold Reduction ◽  
Previous State ◽  
Microscopy Images ◽  
Field View

AbstractReflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4–5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved $$88.07\%$$ 88.07 % classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

scholarly journals Gesture recognition system based on CNN-IndRNN and OpenBCI

2021 ◽  
Vol 336 ◽  
pp. 06003
Author(s):  
Na Wu ◽  
Hao JIN ◽  
Xiachuan Pei ◽  
Shurong Dong ◽  
Jikui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition System ◽  
Classification Performance ◽  
Non Invasive ◽  
Average Accuracy ◽  
Open Source Hardware ◽  
Semg Signals

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.

scholarly journals ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

2020 ◽  
Vol 34 (05) ◽  
pp. 8180-8187 ◽  
Author(s):  
Fei Li ◽  
Hong Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Evaluation Metrics ◽  
Research Attention ◽  
Clinical Text ◽  
Previous State ◽  
Mimic Iii ◽  
Icd Coding ◽  
Document Representations

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.

scholarly journals Methodology for Efficient CNN Architectures in Profiling Attacks

2019 ◽  
pp. 1-36
Author(s):  
Gabriel Zaid ◽  
Lilian Bossuet ◽  
Amaury Habrard ◽  
Alexandre Venelli
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Side Channel ◽  
Clear Understanding ◽  
Learning Techniques ◽  
Previous State ◽  
Template Attacks ◽  
Public Datasets ◽  
Visualization Techniques

The side-channel community recently investigated a new approach, based on deep learning, to significantly improve profiled attacks against embedded systems. Previous works have shown the benefit of using convolutional neural networks (CNN) to limit the effect of some countermeasures such as desynchronization. Compared with template attacks, deep learning techniques can deal with trace misalignment and the high dimensionality of the data. Pre-processing is no longer mandatory. However, the performance of attacks depends to a great extent on the choice of each hyperparameter used to configure a CNN architecture. Hence, we cannot perfectly harness the potential of deep neural networks without a clear understanding of the network’s inner-workings. To reduce this gap, we propose to clearly explain the role of each hyperparameters during the feature selection phase using some specific visualization techniques including Weight Visualization, Gradient Visualization and Heatmaps. By highlighting which features are retained by filters, heatmaps come in handy when a security evaluator tries to interpret and understand the efficiency of CNN. We propose a methodology for building efficient CNN architectures in terms of attack efficiency and network complexity, even in the presence of desynchronization. We evaluate our methodology using public datasets with and without desynchronization. In each case, our methodology outperforms the previous state-of-the-art CNN models while significantly reducing network complexity. Our networks are up to 25 times more efficient than previous state-of-the-art while their complexity is up to 31810 times smaller. Our results show that CNN networks do not need to be very complex to perform well in the side-channel context.

scholarly journals A Grammar-Based Structural CNN Decoder for Code Generation

2019 ◽  
Vol 33 ◽  
pp. 7055-7062 ◽  
Author(s):  
Zeyu Sun ◽  
Qihao Zhu ◽  
Lili Mou ◽  
Yingfei Xiong ◽  
Ge Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Programming Language ◽  
Code Generation ◽  
State Of The Art ◽  
Semantic Parsing ◽  
Code Generator ◽  
Percentage Points ◽  
Grammar Rules ◽  
Previous State ◽  
Program Description

Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RNN to capture such a long sequence. In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation. Our model generates a program by predicting the grammar rules of the programming language; we design several CNN modules, including the tree-based convolution and pre-order convolution, whose information is further aggregated by dedicated attentive pooling layers. Experimental results on the HearthStone benchmark dataset show that our CNN code generator significantly outperforms the previous state-of-the-art method by 5 percentage points; additional experiments on several semantic parsing tasks demonstrate the robustness of our model. We also conduct in-depth ablation test to better understand each component of our model.

scholarly journals DeepFacade: A Deep Learning Approach to Facade Parsing

2017 ◽  
Author(s):  
Hantang Liu ◽  
Jialiang Zhang ◽  
Jianke Zhu ◽  
Steven C. H. Hoi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Novel ◽  
Semantic Categories ◽  
The Neural Network ◽  
Building Facades ◽  
Previous State ◽  
Bounding Boxes

The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region Proposal Network. We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the eTRIMS dataset. As far as we know, we are the first to employ end-to-end deep convolutional neural network on full image scale in the task of building facades parsing.

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