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检索条件"主题词=Encoder-decoder"

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Fully Convolutional encoder-decoder Architecture (FCEDA) for Skin Lesions Segmentation 11th

Fully Convolutional Encoder-Decoder Architecture (FCEDA) for...

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11th International Conference on Computational Collective Intelligence (ICCCI)

作者： Adegun, Adekanmi Viriri, Serestina Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa

ISBN: (纸本)9783030283773;9783030283766

Segmentation which is identification of regions of interest (ROIs) in medical images is a very important step for image analysis in computer-aided diagnosis systems. Accurate segmentation of skin lesions images plays a vital role in efficient diagnosis of melanoma skin cancer. Diagnosis of melanoma cancer through the segmentation of skin lesions is a challenging task due to possible presence of noise and artefacts such as hairs, air or oil bubbles on the skin lesion images. Skin lesions images are also sometimes characterized with weak edges, irregular and fuzzy borders, marks, dark corners, skin lines and blood vessels on skin lesions. Recently, segmentation methods based on Fully Convolutional encoder-decoder Architecture (FCEDA) have achieved great success in medical images. This work presents automatic skin lesion segmentation method that is based on Fully Convolutional encoder-decoder Architecture. Two types of FCEDA namely U-Net and SegNet architectures, have been examined and utilized for segmentation of skin lesion images. The performance analysis of the two architectures have been conducted. Evaluation and comparison of these two architectures were also carried out. This work finds out and proposes possible improvements of these methods on the segmentation of skin lesions. It is also a systematic comparison of U-Net and SegNet models on the segmentation of skin lesion images. The paper discovers how deep learning methods can be explored using a supervised approach to get accurate results with less complexity possible. The models were evaluated on skin lesion challenge dataset in ISIC 2018 dermoscopic images archives.

关键词： Melanoma U-Net Deep learning FCEDA encoder-decoder SegNet Segmentation

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EDLAD: AN encoder-decoder LONG SHORT-TERM MEMORY NETWORK-BASED ANOMALY DETECTOR FOR HYPERSPECTRAL IMAGES

EDLAD: AN ENCODER-DECODER LONG SHORT-TERM MEMORY NETWORK-BAS...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhu, Dehui Du, Bo Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

ISBN: (纸本)9781665403696

In this paper, an encoder-decoder long short-term memory network-based anomaly detector (denoted as EDLAD) is proposed for hyperspectral images. The proposed EDLAD aims to simultaneously alleviate anomaly contamination and build a stable background component for anomaly detection. To reduce anomaly contamination, the EDLAD first utilizes a well-designed encoder-decoder LSTM to reconstruct the hyperspectral image. Based on the concept that the anomaly pixels occupy an extremely small fraction of the image, the well-designed encoder-decoder LSTM network tends to maintain the background and alleviate anomaly during the reconstruction process since the whole image is employed for training the network. Then the dimension reduction is used to further alleviate the anomaly contamination and build a stable background component. Finally, the EDLAD applies the Mahalanobis distance differences to detect the probable anomalies. The experiments on two benchmark hyperspectral images demonstrate the superiority of the EDLAD in anomaly detection.

关键词： Hyperspectral imagery (HSI) anomaly detection deep learning encoder-decoder long short-term memory (LSTM)

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ATTENTION-BASED encoder-decoder NETWORK FOR SINGLE IMAGE DEHAZING

ATTENTION-BASED ENCODER-DECODER NETWORK FOR SINGLE IMAGE DEH...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Gao, Shunan Zhu, Jinghua Xi, Heran Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665449892

Single image dehazing is a challenging problem in computer vision due to it is highly ill-posed. Although recent research has made great progress, the dehazed images produced by existing models still have residual haze and lose too much detail information. To solve the above problems, we propose an end-to-end Attention-based encoder-decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise feature. This attention mechanism is integrated in residual block which is the core feature extraction module of encoder-decoder. Extensive experiments on synthetic datasets and real-hazy images demonstrate that our AEDNet achieves better performance compared with previous state-of-the-art methods.

关键词： Single image dehazing encoder-decoder channel shuffle attention image detail restoration

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Pixel-Semantic Revising of Position: One-Stage Object Detector with Shared encoder-decoder 27th

Pixel-Semantic Revising of Position: One-Stage Object Detect...

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27th International Conference on Neural Information Processing

作者： Li, Qian Guo, Nan Ye, Xiaochun Fan, Dongrui Tang, Zhimin Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030638191;9783030638207

Recently, many methods have been proposed for object detection. However, they cannot detect objects by semantic features, adaptively. According to channel and spatial attention mechanisms, we mainly analyze that different methods detect objects adaptively. Some state-of-the-art detectors combine different feature pyramids with many mechanisms. However, they require more cost. This work addresses that by an anchor-free detector with shared encoder-decoder with attention mechanism, extracting shared features. We consider features of different levels from backbone (e.g., ResNet-50) as the basis features. Then, we feed the features into a simple module, followed by a detector header to detect objects. Meantime, we use the semantic features to revise geometric locations, and the detector is a pixel-semantic revising of position. More importantly, this work analyzes the impact of different pooling strategies (e.g., mean, maximum or minimum) on multi-scale objects, and finds the minimum pooling can improve detection performance on small objects better. Compared with state-of-the-art MNC based on ResNet-101 for the standard MSCOCO 2014 baseline, our method improves detection AP of 3.8%.

关键词： Detection encoder-decoder Attention mechanism Pooling

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Drug Reaction Discriminator within encoder-decoder Neural Network Model: COVID-19 Pandemic Case Study 7

Drug Reaction Discriminator within Encoder-Decoder Neural Ne...

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7th International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Grissette, Hanane Nfaoui, El Habib Sidi Mohamed Ben Abdellah Univ Fac Sci LISAC Lab Fes Morocco

ISBN: (纸本)9780738111803

Social networks become widely used for understanding patients shared experiences, and reaching a vast audience in a matter of seconds. In particular, many health-related organizations used sentiment analysis to automatically reporting treatment issues, drug misuse, new infectious disease symptoms. Few approaches have proposed in this matter, especially for detecting different drug reaction descriptions from patients generated narratives on social networks. Most of them consisted of only detecting adverse drug reaction(ADR), but may fail to retrieve other aspect, e.g, the beneficial drug reaction or drug retroviral effects such as "relieve intraocular pressure associated with glaucoma". In this study, we propose to develop an encoder-decoder for drug reaction discrimination that involves an enhanced distributed biomedical representation from controlled medical vocabulary such as PubMed and Clinical note MIMIC III. The embedding mechanism primarily leverages contextual information and learn from predefined clinical relationships in term of medical conditions in order to define possible drug reaction of individual meaning and multi-word expressions in the field of distributional semantics configuration that clarifies sentence's similarity in the same contextual target space, which are further share semantically common drug description meanings. Furthermore, the bidirectional sentiment inductive model are created to enhance drug reactions vectorization from real-world patients description whereby achieved higher performance in terms of disambiguating false positive and/or negative assessments. As a result, we achieved an 85.2% accuracy performance and the architecture shows a well-encoding of real-world drug entities descriptions.

关键词： Biomedical distributed representation Drug reaction encoder-decoder medical concepts Sentiment information

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DEEP PATCH-BASED PRIORS UNDER A FULLY CONVOLUTIONAL encoder-decoder ARCHITECTURE FOR INTERSTITIAL LUNG DISEASE SEGMENTATION 15

DEEP PATCH-BASED PRIORS UNDER A FULLY CONVOLUTIONAL ENCODER-...

引用

15th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Vakalopoulou, M. Chassagnon, G. Paragios, N. Revel, M. -P. Zacharaki, E. I. Univ Paris Saclay Cent Supelec CVN Paris France TheraPanacea Paris France Univ Patras Dept Comp Engn & Informat Patras Greece Univ Paris 05 Grp Hosp Cochin Dept Radiol Hotel Dieu Paris France

ISBN: (纸本)9781538636367

Interstitial lung diseases (ILD) encompass a large spectrum of diseases sharing similarities in their physiopathology and computed tomography (CT) appearance. In this paper, we propose the adaption of a deep convolutional encoder-decoder (CED) that has shown high accuracy for image segmentation. Such architectures require annotation of the total region with pathological findings. This is difficult to acquire, due to uncertainty in the definition and extent of disease patterns and the need of significant human effort, especially for large datasets. Therefore, often current methods use patch-based implementations of convolutional neural networks, which however tend to produce spatially inhomogeneous segmentations due to their local contextual view. We exploit the advantages of both architectures by using the output of a patch-based classifier as a prior to a CED. Our method could advance the state-of-the-art in lung tissue segmentation using only a small number of newly annotated images.

关键词： encoder-decoder probabilistic priors pixelwise classification

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SED: A Simple encoder-decoder for Open-Vocabulary Semantic Segmentation

SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic S...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, Bin Cao, Jiale Xie, Jin Khan, Fahad Shahbaz Pang, Yanwei Tianjin Univ Tianjin Peoples R China Chongqing Univ Chongqing Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ Linkoping Sweden Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models, in which the key is to adapt the image-level model for pixel-level segmentation task. In this paper, we propose a simple encoder-decoder, named SED, for open-vocabulary semantic segmentation, which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection. The hierarchical encoder-based cost map generation employs hierarchical backbone, instead of plain transformer, to predict pixel-level image-text cost map. Compared to plain transformer, hierarchical backbone better captures local spatial information and has linear computational complexity with respect to input size. Our gradual fusion decoder employs a top-down structure to combine cost map and the feature maps of different backbone levels for segmentation. To accelerate inference speed, we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder, resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets, which demonstrates the efficacy of our SED method. When using ConvNeXt-B, our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond (ms) per image on a single A6000. Our source code is available at https://***/xb534/SED.

关键词： encoder-decoder Open-Vocabulary Semantic Segmentation

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Segmental encoder-decoder Models for Large Vocabulary Automatic Speech Recognition 19

Segmental Encoder-Decoder Models for Large Vocabulary Automa...

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19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Beck, Eugen Hannemann, Mirko Doetsch, Patrick Schlueter, Ralf Ney, Hermann Rhein Westfal TH Aachen Comp Sci Dept Human Language Technol & Pattern Recognit D-52062 Aachen Germany

ISBN: (纸本)9781510872219

It has been known for a long time that the classic Hidden-Markov-Model (HMM) derivation for speech recognition contains assumptions such as independence of observation vectors and weak duration modeling that are practical but unrealistic. When using the hybrid approach this is amplified by trying to fit a discriminative model into a generative one. Hidden Conditional Random Fields (CRFs) and segmental models (e.g. Semi-Markov CRFs / Segmental CRFs) have been proposed as an alternative, but for a long time have failed to get traction until recently. In this paper we explore different length modeling approaches for segmental models, their relation to attention-based systems. Furthermore we show experimental results on a handwriting recognition task and to the best of our knowledge the first reported results on the Switchboard 300h speech recognition corpus using this approach.

关键词： automatic speech recognition (ASR) (hidden) conditional random fields segmental models encoder-decoder attention mechanism

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A Study of the Recurrent Neural Network encoder-decoder for Large Vocabulary Speech Recognition 16

A Study of the Recurrent Neural Network Encoder-Decoder for ...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Lu, Liang Zhang, Xingxing Cho, Kyunghyun Renals, Steve Univ Edinburgh Ctr Speech Technol Res Edinburgh Midlothian Scotland Univ Edinburgh Inst Language Cognit & Computat Edinburgh Midlothian Scotland Univ Montreal Montreal Inst Learning Algorithms Montreal PQ Canada

ISBN: (纸本)9781510817906

Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combined with hidden Markov models (HMMs). Recently there has been interest in using systems based on recurrent neural networks (RNNs) to perform sequence modelling directly, without the requirement of an HMM superstructure. In this paper, we study the RNN encoder-decoder approach for large vocabulary end-to-end speech recognition, whereby an encoder transforms a sequence of acoustic vectors into a sequence of feature representations, from which a decoder recovers a sequence of words. We investigated this approach on the Switchboard corpus using a training set of around 300 hours of transcribed audio data. Without the use of an explicit language model or pronunciation lexicon, we achieved promising recognition accuracy, demonstrating that this approach warrants further investigation.

关键词： end-to-end speech recognition deep neural networks recurrent neural networks encoder-decoder

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Deep Architecture Based Spalling Severity Detection System Using encoder-decoder Networks 17th

Deep Architecture Based Spalling Severity Detection System U...

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17th International Symposium on Visual Computing (ISVC)

作者： Yasmin, Tamanna Le, Chuong Hung Manh La Univ Nevada Reno NV 89557 USA

ISBN: (纸本)9783031207150;9783031207167

Proper maintenance of concrete structures is a significant issue to avoid any hazardous situation in civil infrastructure. Spalling is a significant surface concrete distress in bridges and buildings. Correctly detecting the severity level of spalling can make it happen to detect and maintain the harmful spalling promptly to avoid any accidents [10]. While previous works have been on surface defects, like cracks and spallings, few have addressed spalling severity detection. In this paper, we have proposed a deep learning-based approach to detect the exact location of spalling according to severity level by using pixel-by-pixel classification. Our network labels each pixel as no-spalling, small, or large spalling. To get the optimal proposed deep architecture, we tested several encoder-decoder networks to compare and analyze the performance of the detection processes.

关键词： Spalling detection Spalling severity Deep architecture encoder-decoder

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