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ARRHYTHMIA CLASSIFICATION WITH HEARTBEAT-AWARE TRANSFORMER

ARRHYTHMIA CLASSIFICATION WITH HEARTBEAT-AWARE TRANSFORMER

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wang, Bin Liu, Chang Hu, Chuanyan Liu, Xudong Cao, Jun Lepu Med Technol Beijing Peoples R China

ISBN: (纸本)9781728176055

Electrocardiography (ECG) is a conventional method in arrhythmia diagnosis. In this paper, we proposed a novel neural network model which treats typical heartbeat classification task as 'Translation' problem. By introducing Transformer structure into model, and adding heartbeat-aware attention mechanism to enhance the alignment between encoded sequence and decoded sequence, after trained with ECG database, (which are collected from 200k patients in over 2000 hospitals for more than 10 years), the validation result of independent test dataset shows that this new heartbeat-aware Transformer model can outperform classic Transformer and other sequence to sequence methods. Finally, we show that the visualization of encoder-decoder attention weights provides more interpretable information about how a Transformer make a diagnosis based on raw ECG signals, which has guiding significance in clinical diagnosis.

关键词： Transformer encoder-decoder ECG Heartbeat-Aware Time Series

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SHOW AND SPEAK: DIRECTLY SYNTHESIZE SPOKEN DESCRIPTION OF IMAGES

SHOW AND SPEAK: DIRECTLY SYNTHESIZE SPOKEN DESCRIPTION OF IM...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wang, Xinsheng Feng, Siyuan Zhu, Jihua Hasegawa-Johnson, Mark Scharenborg, Odette Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Delft Univ Technol Multimedia Comp Grp Delft Netherlands Univ Illinois Dept Elect & Comp Engn Urbana IL USA

ISBN: (纸本)9781728176055

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that describes this image. The final speech audio is obtained from the predicted spectrogram via WaveNet. Extensive experiments on the public benchmark database Flickr8k demonstrate that the proposed SAS is able to synthesize natural spoken descriptions for images, indicating that synthesizing spoken descriptions for images while bypassing text and phonemes is feasible.

关键词： Image-to-speech image captioning speech synthesis sequence-to-sequence encoder-decoder

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A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition 10th

A Blended Attention-CTC Network Architecture for Amharic Tex...

引用

10th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Belay, Birhanu Hailu Habtegebrial, Tewodros Liwicki, Marcus Belay, Gebeyehu Stricker, Didier Tech Univ Kaiserslautern Kaiserslautern Germany Lulea Univ Technol Lulea Sweden Bahir Dar Inst Technol Bahir Dar Ethiopia DFKI Augmented Vis Dept Kaiserslautern Germany

ISBN: (纸本)9789897584862

In this paper, we propose a blended Attention-Connectionist Temporal Classification (CTC) network architecture for a unique script, Amharic, text-image recognition. Amharic is an indigenous Ethiopic script that uses 34 consonant characters with their 7 vowel variants of each and 50 labialized characters which are derived, with a small change, from the 34 consonant characters. The change involves modifying the structure of these characters by adding a straight line, or shortening and/or elongating one of its main legs including the addition of small diacritics to the right, left, top or bottom of the character. Such a small change affects orthographic identities of character and results in shape similarly among characters which are interesting, but challenging task, for OCR research. Motivated with the recent success of attention mechanism on neural machine translation tasks, we propose an attention-based CTC approach which is designed by blending attention mechanism directly within the CTC network. The proposed model consists of an encoder module, attention module and transcription module in a unified framework. The efficacy of the proposed model on the Amharic language shows that attention mechanism allows learning powerful representations by integrating information from different time steps. Our method outperforms state-of-the-art methods and achieves 1.04% and 0.93% of the character error rate on ADOCR test datasets.

关键词： Amharic Script Blended Attention-CTC BLSTM CNN encoder-decoder Network Architecture OCR Pattern Recognition

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Historical Report Assist Medical Report Generation 14

Historical Report Assist Medical Report Generation

引用

14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Ye, Shan Wang, Mei Dong, Yijie Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Ruijin Hosp Dept Ultrasound Shanghai Peoples R China

ISBN: (纸本)9789897584909

How to automatically generate diagnostic reports with accurate content, standardized structure and clear semantics, brings great challenges due to the complexity of medical images and the detailed paragraph descriptions for medical images. The structure and the semantic contents of the historical report are very helpful for the current report generation. This paper proposes a text report generation method assisted by historical reports. In the proposed method, both the previous report and the keywords generated from the current images are modeled by using two encoders respectively. The co-attention mechanism is introduced to jointly learn the historical reports and the keywords. The decoder based on the co-attention is used to generate a long description of the image. The progress that learns from the historical report and the current report in the training set helps to generate an accurate report for the new image. Furthermore, the structure in the historical report helps to generate a more natural text report. We conducted experiments on the practical ultrasound data, which is provided by a prestigious hospital in China. The experimental results show that the reports generated by the proposed method are closer to the reports generated by radiologists.

关键词： Automatic Report Generation Historical Report encoder-decoder Co-attention

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DELINEATION OF CORONARY STENTS IN INTRAVASCULAR ULTRASOUND PULLBACKS

DELINEATION OF CORONARY STENTS IN INTRAVASCULAR ULTRASOUND P...

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Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Wissel, Tobias Riedl, Katharina A. Schaefers, Klaus Nickisch, Hannes Brunner, Fabian J. Schnellbaecher, Nikolas Blankenberg, Stefan Seiffert, Moritz Grass, Michael Philips Res Hamburg Hamburg Germany Philips Res Eindhoven Eindhoven Netherlands Univ Heart & Vasc Ctr Hamburg Dept Cardiol Hamburg Germany German Ctr Cardiovasc Res DZHK Partner Site Hamburg Lubeck Kiel Hamburg Germany

ISBN: (纸本)9781510640269

Ischemic heart disease remains one of the leading causes of death worldwide. Percutaneous coronary interventions (PCIs) for implanting coronary stents are preferred for patients with acute myocardial infarction but may also be performed in patients with chronic coronary syndromes to improve symptoms and outcome. During the PCI, the assessment of stent apposition, evaluation of in-stent restenosis or guidance for complex stenting of bifurcation lesions may be improved by intravascular imaging such as intravascular ultrasound (IVUS). However, advanced interpretation of the image often requires expertise and training. To approach this issue, we introduce an automatic delineation of stent struts within the IVUS pullback. We propose a cascaded segmentation based on data-driven learning with a neural encoder-decoder architecture. The learning process uses 80 IVUS sequences from 28 patients which were acquired and partially annotated by the Department of Cardiology, University Heart & Vascular Center Hamburg, Germany. The annotations include 1108, 555 and 355 frames with delineated lumen, stent and calcium as well as 13696 and 10689 frame-wise stent and no-stent indications. The network was pre-trained on lumen segmentation and refined to first identify stent frames using an encoder network and subsequently segment the struts with a decoder. Quantitative evaluation using 3-fold cross-validation revealed 88.3% precision, 92.4% recall and 0.824 Dice for the encoder and 67.0%, 60.3% and 0.611 for the decoder. We conclude that the encoder successfully leverages the larger number of high-level annotations to reject non-stent frames avoiding unnecessary false positives for the decoder trained on much less, but fine-granular annotations.

关键词： Intravascular ultrasound stent convolutional neural networks segmentation detection encoder-decoder

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DSNet: Dynamic Selection Network for Biomedical Image Segmentation 30th

DSNet: Dynamic Selection Network for Biomedical Image Segmen...

引用

30th International Conference on Artificial Neural Networks (ICANN)

作者： Qin, Xiaofei Liu, Yan Tang, Liang Zhao, Shuhui Zhou, Xingchen Zhang, Xuedian Wang, Dengbin Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Shanghai Key Lab Contemporary Opt Syst Shanghai Peoples R China Minist Educ Key Lab Biomed Opt Technol & Devices Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Xinhua Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9783030863654;9783030863647

This paper focuses on uterine segmentation, an important clue for understanding MRI images and medical analysis of expectant mothers, which has long been underestimated. Related works have proven that the receptive field is crucial in computer vision. However, current methods usually use pooling operations to continuously enlarge the receptive field, which leads to some inevitable information loss. In this paper, we design the Dynamic Selection Module (DSM) to effectively capture the spatial perception of medical images. Specifically, DSM adopts dynamic convolution kernel to adaptively adjust the receptive field in the horizontal and vertical directions. We then combine DSM and residual block to construct a Dynamic Residual Unit (DRU) which further learns feature representation. Then DRU is embedded in the standard U-Net termed Dynamic Selection Network (DSNet). We evaluate our method on the Uterus dataset we acquired. To validate the generalization of this method, we also do the same experiment on the Gland Segmentation dataset and Lung dataset. The results demonstrate that DSNet can significantly boost the performance of medical image segmentation than other related encoder-decoder architectures.

关键词： Medical image segmentation Adaptive receptive field encoder-decoder

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A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement 22

A Causal U-net based Neural Beamforming Network for Real-Tim...

引用

Interspeech Conference

作者： Ren, Xinlei Zhang, Xu Chen, Lianwu Zheng, Xiguang Zhang, Chen Guo, Liang Yu, Bing Kuai Shou Technol Co Beijing Peoples R China

ISBN: (纸本)9781713836902

People are meeting through video conferencing more often. While single channel speech enhancement techniques are useful for the individual participants, the speech quality will be significantly degraded in large meeting rooms where the far-field and reverberate conditions are introduced. Approaches based on microphone array signal processing are proposed to explore the inter-channel correlation among the individual microphone channels. In this work, a new causal U-net based multiple-inmultiple-out structure is proposed for real-time multi-channel speech enhancement. The proposed method incorporates the traditional beamforming structure with the multi-channel causal U-net by explicitly adding a beamforming operation at the end of the neural beamformer. The proposed method has entered the INTERSPEECH Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing. With 1.97M model parameters and 0.25 real-time factor on Intel Core i7 (2.6GHz) CPU, the proposed method has outperforms the baseline system of this challenge on PESQ, Si-SNR and STOI metrics.

关键词： multi-channel speech enhancement U-NET encoder-decoder deep learning

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SDPredictNet-A Topology based SDN Neural Routing Framework with Traffic Prediction Analysis 11

SDPredictNet-A Topology based SDN Neural Routing Framework w...

引用

IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Sanagavarapu, Sowmya Sridhar, Sashank Anna Univ Dept Comp Sci & Engn Coll Engn Guindy Chennai 600025 Tamil Nadu India

ISBN: (数字)9781665414906

ISBN: (纸本)9781665414906

Software Defined Networking is an intelligent network management approach for monitoring and improving performance such as in cloud computing. These networks follow separation between the Forwarding layer and the Control layer in the system to enable efficient programmability to perform network configurations. The SDN Controller present in the network has complete information about the network schema and its components to update the routing information of the switches present. The dynamic nature of traffic in these networks with multi-layer switches hinder the efficiency of SDN's performance in multi-cloud environments. This paper proposes SDPredictNet, a Recurrent Neural Network framework deployed on the SDN Controller that can predict the traffic in the network and update flow tables of the higher layer switches to perform routing based on the perceived bottlenecks in the network. SDPredictNet uses a Sequence-to-Sequence model that trains on the network data congestion to forecast the traffic in the SDN which is then modelled by an Artificial Neural Network to predict the path of the packets. SDPredictNet has achieved a RMSE score of 0.07 and an accuracy of 99.88% for traffic estimation and subsequent path determination.

关键词： Software Defined Networks Traffic Prediction Multi-layer switches Deep Neural Routing LSTM Sequence to Sequence Modelling encoder-decoder

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Exploring Cross-lingual Singing Voice Synthesis Using Speech Data 12

Exploring Cross-lingual Singing Voice Synthesis Using Speech...

引用

12th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Cao, Yuewen Liu, Songxiang Kang, Shiyin Hu, Na Liu, Peng Liu, Xunying Su, Dan Yu, Dong Meng, Helen Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Huya Inc Guangzhou Peoples R China

ISBN: (纸本)9781728169941

State-of-the-art singing voice synthesis (SVS) models can generate natural singing voice of a target speaker, given his/her speaking/singing data in the same language. However, there may be challenging conditions where only speech data in a non-target language of the target speaker is available. In this paper, we present a cross-lingual SVS system that can synthesize an English speaker's singing voice in Mandarin from musical scores with only her speech data in English. The proposed cross-lingual SVS system contains four parts: a BLSTM based duration model, a pitch model, a cross-lingual acoustic model and a neural vocoder. The acoustic model employs encoder-decoder architecture conditioned on pitch, phoneme duration, speaker information and language information. An adversarially-trained speaker classifier is employed to discourage the text encodings from capturing speaker information. Objective evaluation and subjective listening tests demonstrate that the proposed cross-lingual SVS system can generate singing voice with decent naturalness and fair speaker similarity. We also find that adding singing data or multi-speaker monolingual speech data further improves generalization on pronunciation and pitch accuracy.

关键词： singing voice synthesis cross-lingual encoder-decoder adversarial loss

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Segmentation of Prostate in MRI Images Using Depth Separable Convolution Operations 12th

Segmentation of Prostate in MRI Images Using Depth Separable...

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12th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Khan, Zia Yahya, Norashikin Alsaih, Khaled Meriaudeau, Fabrice Univ Teknol PETRONAS Ctr Intelligent & Imaging Res CISIR Dept Elect & Elect Engn Seri Iskandar Perak Malaysia Univ Bourgogne Franche Comte ImViA IFTIM Besancon France

ISBN: (纸本)9783030684495;9783030684488

The segmentation of the prostate gland into two sub-regions, namely, the central gland (CG) and the peripheral zone (PZ) is crucial for the prostate cancer (PCa) diagnosis. The nature and occurrence of cancer occurred in the prostate is substantially different in both zones. Magnetic resonance imaging modality (MRI) is a clinically primary tool for computer-based assessment and remediation of various cancer types such as PCa. In this paper, we evaluated DeeplabV3+ model on T2W MRI scans using the I2CVB dataset, which is designed in an encoderdecoder style for the zonal segmentation of prostate regions. An important feature of DeeplabV3+ is the depth-wise separable convolutions, which allow more information to be extracted from images as it uses filters with different dilation rates. Prior to being fed to the deep neural network, image pre-processing techniques are applied, including image resizing, cropping, and denoising. The DeeplabV3+ model performance is evaluated using the Dice similarity coefficient (DSC) metric and compared with the vanilla U-Net architecture. Results show that the encoderdecoder network having depth-wise separable convolutions performed better prostate segmentation than the network with standard convolution operations with the DSC value of 70.1% in PZ and 81.5% in CG zone.

关键词： Zonal segmentation MRI encoder-decoder U-Net DeeplabV3+

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