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12th International Workshop on Machine Learning in Medical Imaging (MLMI 2021)

作者： Wang, Dayang Wu, Zhan Yu, Hengyong Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA

ISBN: (纸本)9783030875886;9783030875893

Low dose computed tomography (CT) is a mainstream for clinical applications. However, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more artifacts which are obstacles for practical applications. In the last few years, convolution-based end-to-end deep learning methods have been widely used for LDCT image denoising. Recently, transformer has shown superior performance over convolution with more feature interactions. Yet its applications in LDCT denoising have not been fully cultivated. Here, we propose a convolution-free T2T vision transformer-based encoderdecoder Dilation Network (TED-Net) to enrich the family of LDCT denoising algorithms. The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer. Our model (Codes are available at https://***/wdayang/TED- Net) is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.

关键词： Low-dose CT Transformer Token-to-Token encoder-decoder Dilation

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Micro-climate Prediction - Multi Scale encoder-decoder based Deep Learning Framework 21

Micro-climate Prediction - Multi Scale Encoder-decoder based...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Kumar, Peeyush Chandra, Ranveer Bansal, Chetan Kalyanaraman, Shivkumar Ganu, Tanuja Grant, Michael Microsoft Res Redmond WA 98052 USA Microsoft Bengaluru India Microsoft Res Bengaluru India Univ Washington Seattle WA 98195 USA Climate Corp San Francisco CA USA

ISBN: (纸本)9781450383325

This paper presents a deep learning approach for a versatile Microclimate prediction framework (DeepMC). Micro climate predictions are of critical importance across various applications, such as Agriculture, Forestry, Energy, Search & Rescue, etc. To the best of our knowledge, there is no other single framework which can accurately predict various micro-climate entities using Internet of Things (IoT) data. We present a generic framework (DeepMC) which predicts various climatic parameters such as soil moisture, humidity, wind speed, radiation, temperature based on the requirement over a period of 12 hours - 120 hours with a varying resolution of 1 hour - 6 hours, respectively. This framework proposes the following new ideas: 1) Localization of weather forecast to IoT sensors by fusing weather station forecasts with the decomposition of IoT data at multiple scales and 2) A multi-scale encoder and two levels of attention mechanisms which learns a latent representation of the interaction between various resolutions of the IoT sensor data and weather station forecasts. We present multiple real-world agricultural and energy scenarios, and report results with uncertainty estimates from the live deployment of DeepMC, which demonstrate that DeepMC outperforms various baseline methods and reports 90%+ accuracy with tight error bounds.

关键词： Micro-climate prediction Sustainability Deep learning encoder-decoder Attention mechanism Transfer learning IoT sensors Sequence to sequence Time series Agriculture Energy Wavelet transform

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3D Multi-Branch encoder-decoder Networks with Attentional Feature Fusion for Pulmonary Nodule Detection in CT Scans

3D Multi-Branch Encoder-Decoder Networks with Attentional Fe...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Chenjiao Wang, Lulu Wu, Xing He, Zhongshi Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9780738133669

Pulmonary nodule detection in low-dose computed tomography (CT) images is essential for early screening and treatment of lung cancer. Previous related researches based on deep convolutional neural networks generally rely on 2D or 2.5D components and only focus on the output feature information under a single receptive field. Considering the 3D nature of lung CT images and the performance limitation of state-of-the-art nodule detection methods, we develop a novel 3D multi-branch region proposal network with an encoder-decoder structure. Specifically, each parallel branch is designed with 3D residual blocks and U-Net-like structure to effectively extract multi-scale fusion features based on 3D spatial information of CT scans, and the strategies of varying receptive fields and sharing weight parameters are used to improve the sensitivity of the detection network to nodules with scale variation and maintain the original parameters. Besides, we propose a multi-scale attentional feature fusion module to better fuse high-resolution and semantically strong features and adaptively learn the inter-dependency information of different feature maps. Finally, we compare a dynamically scaled cross entropy loss and online hard example mining (OHEM) to combat the imbalance of positive and negative samples during training, which is aimed at assisting with network optimization. Our extensive experiments on publicly available CT scans obtained from LUNA16 and TianChi(1) competition dataset demonstrate that our method outperform state-of-the-art pulmonary nodule detection models.

关键词： pulmonary nodule detection 3d convolutional neural network encoder-decoder multi-scale attentional feature fusion

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Constrained Image Splicing Detection and Localization With Attention-Aware encoder-decoder and Atrous Convolution

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IEEE ACCESS 2020年 8卷 6729-6741页

作者： Liu, Yaqi Zhao, Xianfeng Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100093 Peoples R China

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task and plays an important role in verifying the generating process of a forged image. CISDL conducts dense matching between two investigated images and detects whether one image has forged regions pasted from the other. In this work, we introduce a novel attention-aware encoder-decoder deep matching network named as AttentionDM for CISDL. An encoder-decoder with atrous convolution is newly designed for hierarchical features dense matching and fine-grained masks generation. A novel attention-aware correlation computation module is built on normalization operations and informative features recalibration with channel attention blocks. Last but not least, VGG and ResNets are respectively formulated as feature extractors for comprehensive comparisons in CISDL. Extensive experiments demonstrate the superior performance of AttentionDM over the state-of-the-art methods.

关键词： encoder-decoder atrous convolution normalization channel attention

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Pedestrian behavior prediction model with a convolutional LSTM encoder-decoder

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 560卷 125132-125132页

作者： Chen, Kai Song, Xiao Han, Daolin Sun, Jinghan Cui, Yong Ren, Xiaoxiang Beihang Univ BUAA Beijing Peoples R China Univ Illinois Urbana IL USA Nanan Primary Sch Xian Shanxi Peoples R China

Pedestrian behavior modeling is a challenging problem especially in crowded transportation scenarios. Some recent studies have addressed this problem using deep neural network, but the accuracy of trajectory prediction is still not high because the internal structure of the typical deep neural network with long short-term memory (LSTM) is a one-dimensional vector, which destroys the spatial information around a pedestrian. Therefore, these models cannot fully learn spatial sensing behavior of pedestrians. To solve this, we recommend using multi-channel tensors to represent the environmental information of pedestrians. Meanwhile, the spatiotemporal interactions among the pedestrians are represented by convolution operations of these tensors. Then, an end-to-end fully convolutional LSTM encoder-decoder is designed, trained and tested. Finally, our approach is compared with existing LSTM-based methods using five crowded video sequences with public datasets. The results show that our method reduces the displacement offset error and provides more realistic trajectory prediction in manifold cases. (c) 2020 Published by Elsevier B.V.

关键词： Pedestrian behavior model Trajectory prediction Long short-term memory Convolution encoder-decoder

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ED-DRAP: encoder-decoder Deep Residual Attention Prediction Network for Radar Echoes

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Che, Hongshu Niu, Dan Zang, Zengliang Cao, Yichao Chen, Xisong Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China PLA Univ Sci & Technol Inst Meteorol & Oceanog Nanjing 211101 Peoples R China

Precipitation nowcasting is quite important and fundamental. It underlies various public services ranging from rainstorm warnings to flight safety. In order to further improve the prediction accuracy for the spatiotemporal sequence forecasting problem, we propose an encoder-decoder deep residual attention prediction network, which adaptively rescales the multiscale sequence- and spatial-wise features and achieves very deep trainable residual prediction by integrating global residual learning and local deep residual sequence and spatial attention blocks (RSSABs). Experiments in a real-world radar echo map dataset of South China show that compared with the ingenious PredRNN++, TrajGRU methods, and newly proposed Unet-based methods, our ED-DRAP network performs better on the precipitation nowcasting metrics, as well as occupies small GPU memory.

关键词： Feature extraction Spatiotemporal phenomena Radar Decoding Forecasting Three-dimensional displays Radar remote sensing Deep residual prediction encoder-decoder precipitation nowcasting sequence and spatial attention

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encoder-decoder Based CNN Structure for Microscopic Image Identification 27th

Encoder-Decoder Based CNN Structure for Microscopic Image Id...

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27th International Conference on Neural Information Processing

作者： Polap, Dawid Wozniak, Marcin Korytkowski, Marcin Scherer, Rafal Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland Czestochowa Tech Univ Dept Intelligent Comp Syst Czestochowa Poland

ISBN: (纸本)9783030638290;9783030638306

The significant development of classifiers has made object detection and classification by using neural networks more effective and more straightforward. Unfortunately, there are images where these operations are still difficult due to the overlap of objects or very blurred contours. An example is images obtained from various microscopes, where bacteria or other biological structures can merge, or even have different shapes. To this end, we propose a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in such images. In our research, we have included elements such as very weak contours of shapes that may result from the merging of biological objects. The presented method was compared with others, such as a faster recurrent convolutional neural network to indicate the advantages of the proposed solution.

关键词： Convolutional neural networks encoder-decoder Bacteria shape detection

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Adding visual attention into encoder-decoder model for multi-modal machine translation

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2023年第2期11卷

作者： Xu, Chun Yu, Zhengqing Shi, Xiayang Chen, Fang Xinjiang Univ Finance & Econ Coll Informat Management Urumqi Peoples R China Zhengzhou Univ Light Ind Zhenghzou Peoples R China Henan Univ Tradit Chinese Med Med Dept Zhengzhou Peoples R China

Multi-modal neural machine translation (MNMT) aims to integrate visual and textual information to translate source sentences into target and attracts a lot of attentions. Existing methods contribute a lot for capturing interactions between visual and textual features to improve the performance of neural machine translation (NMT). However, most of them don't consider the multi-modal consistency for MNMT. In fact, the image provides the semantic global consistency between different languages. We believe that adding bilingual-visual agreement into the encoder and decoder simultaneously can obtain bilingual representations and is useful for NMT. In this paper, we propose to simultaneously integrate visual information in encoder and decoder to learn the interactions between visual and textual features, in this paper called the model VMNMT. As the visual information provide global context, the encoder and decoder can learn the bilingual representations. Besides, we introduce a new bilingual-visual agreement decoder to learn to better representations of corresponding imagesentence pairs. In the experiment, the improvement was 2.02 BLEU on the English-German16 dataset and 1.9 BLEU on the English-German17 dataset. The results show that our method can outperform baselines on several widely-used datasets in terms of various metrics.

关键词： Natural language processing Multi-modal machine translation Bilingual-visual encoder-decoder Bilingual representations

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Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 88卷 115948-115948页

作者： Zhou, Zexun He, Zhongshi Jia, Yuanyuan Du, Jinglong Wang, Lulu Chen, Ziyu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hanshan Normal Univ Coll Comp & Informat Engn Chaozhou 521041 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder-decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder-decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://***/zzxcoder/EvaluationFHEDN.

关键词： Context prior Residual learning Deep convolutional neural network encoder-decoder Face detection

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encoder-decoder with Multi-scale Information Fusion for Semantic Image Segmentation 11

Encoder-Decoder with Multi-scale Information Fusion for Sema...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Ma, Xinxin Liu, Kai Ding, Chongyang Yan, Lin Duan, Meiyu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781510635241

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

关键词： Semantic image segmentation multi-scale context module skip-layer encoder-decoder

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