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Lightweight encoder-decoder model for automatic skin lesion segmentation

Informatics in Medicine Unlocked 2021年 25卷

作者： Wibowo, Adi Purnama, Satriawan Rasyid Wirawan, Panji Wisnu Rasyidi, Hanif Department of Computer Science Diponegoro University Informatics Semarang Indonesia College of Engineering & Computer Science Australian National University Canberra Australia

Accurate skin lesion segmentation (SLS) is an important step in computer-aided diagnosis of melanoma. Automatic detection of skin lesions in dermoscopy images is challenging because of the presence of artifacts and as lesions can have heterogeneous texture, color, and shape with fuzzy or indistinct boundaries. In this study, automatic SLS was performed using a lightweight encoder-decoder, MobileNetV3-UNet, which can achieve high accuracy with low resources. A comprehensive analysis was performed to improve the accuracy of the method in SLS. The semantic segmentation method consists of an encoder-decoder architecture, data augmentation, learning schemes, and post-processing methods. To enhance the SLS, we modified the decoder with the bidirectional ConvLSTM layer from the BCDU-Net and separable blocks from the separable-UNet architecture. Random augmentation was used to improve image diversity in the training dataset to avoid overfitting. Furthermore, a learning scheme based on stochastic weight averaging (SWA) was used to obtain better generalization by averaging multiple local optima. Our method was evaluated using three publicly available datasets, such as ISIC-2017, ISIC-2018, and PH2. We obtained dice coefficient and Jaccard index of 87.74%, 80.25%;91.01%, 83.44%;and 95.18%, 91.08% for ISIC-2017, ISIC-2018, and PH2, respectively. The experimental results proved that the modified MobileNetV3-UNet method can outperform several state-of-the-art methods. © 2021 The Authors

关键词： encoder-decoder MobileNet Random augmentation Skin lesion segmentation Stochastic weight averaging U-net

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Arabic To Bangla Machine Translation Using encoder decoder Approach

Arabic To Bangla Machine Translation Using Encoder Decoder A...

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IEEE-Region-10 Symposium (TENSYMP) - Technology for Impactful Sustainable Development

作者： Kamruzzaman, Toshiba Rajshahi Univ Engn & Technol Elect & Comp Engn Rajshahi Bangladesh

ISBN: (纸本)9781728173665

Machine Translation is one of the sub-fields of computational linguistics which includes broad analytical models enlarged by using extremely advanced semantic *** have converted the Arabic sentences to its corresponding Bangla equivalent sentences using a Neural Machine Translation (NMT) system operated with fixed vocabulary by using Sequence to Sequence (Seq2Seq) mechanism which is structured approach for automatically transferring the source sequences to the target sequences. From experiments,we have achieved 69.76% accuracy in this discipline.

关键词： Machine Translation Arabic to Bangla Sequential to Sequential encoder-decoder RNN

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DSNet:Multi-resolution Dense encoder and Stack decoder Network for Aerial Image Segmentation

DSNet:Multi-resolution Dense Encoder and Stack Decoder Netwo...

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Chinese Automation Congress (CAC)

作者： Chong, Yanwen Nie, Congchong Tao, Yulong Pan, Shaoming Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (纸本)9781728140940

Semantic segmentation in high resolution aerial image is faced with a challenge caused by ubiquitous fine-structure objects. Traditional encoder-decoder structure losses some detail information during the process of down-sampling, which is harmful to the location of fine-structure objects. In this work, we present a multi-resolution dense encoder and stack decoder network to deal with this problem. On the one hand, the dense encoder embeds shallow detailed feature into deep semantic feature through proposed information-reserved down-sampling method called CE-Pooling. On the other hand, the stack decoder gradually enhances the detailed feature through iterative attention fusion. Extensive experiments on several benchmark datasets have been conducted, which shows that our method is superior than the state-of-the-art approaches.

关键词： semantic segmentation encoder-decoder fine-structure ISPRS

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FEATURE FUSION encoder decoder NETWORK FOR AUTOMATIC LIVER LESION SEGMENTATION 16

FEATURE FUSION ENCODER DECODER NETWORK FOR AUTOMATIC LIVER L...

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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Chen, Xueying Zhang, Rong Yang, Pingkun Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Anhui Peoples R China Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA

ISBN: (纸本)9781538636411

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.51) and 3D, based on the dimensionality of the models. However, 2.51) and 31) methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a ***-decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion segmentation from CT images. Our feature fusion method is based on the attention mechanism, which fuses high-level features carrying semantic information with low-level features having image details. Additionally, to compensate for the information loss during the upsampling process, a dense upsampling convolution and a residual convolutional structure are proposed. We tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and achieved competitive results compared with other state-of-the-art methods.

关键词： Liver lesion segmentation,deep learning encoder-decoder attention feature fusion

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ECTC-DOCD: An End-to-end Structure with CTC encoder and OCD decoder for Speech Recognition 20

ECTC-DOCD: An End-to-end Structure with CTC Encoder and OCD ...

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Interspeech Conference

作者： Yi, Cheng Wang, Feng Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijinga Peoples R China

Real-time streaming speech recognition is required by most applications for a nice interactive experience. To naturally support online recognition, a common strategy used in recently proposed end-to-end models is to introduce a blank label to the label set and instead output alignments. However, generating the alignment means decoding much longer than the length of the linguistic sequence. Besides, there exist several blank labels between two output units in the alignment, which hinders models from learning the adjacent dependency of units in the target sequence. In this work, we propose an innovative encoder-decoder structure, called ECTC-DOCD, for online speech recognition which directly predicts the linguistic sequence without blank labels. Apart from the encoder and decoder structures, ECTC-DOCD contains an additional shrinking layer to drop the redundant acoustic information. This layer serves as a bridge connecting acoustic representation and linguistic modelling parts. Through experiments, we confirm that ECTC-DOCD can obtain better performance than a strong CTC model in online ASR tasks. We also show that ECTC-DOCD can achieve promising results on both Mandarin and English ASR datasets with first and second pass decoding.

关键词： end-to-end streaming ASR encoder-decoder OCD CTC

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A multi-task encoder-dual-decoder framework for mixed frequency data prediction

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INTERNATIONAL JOURNAL OF FORECASTING 2024年第3期40卷 942-957页

作者： Lin, Jiahe Michailidis, George Morgan Stanley Machine Learning Res New York NY 10036 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

Mixed -frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available high -frequency data to forecast/nowcast the low -frequency ones. Existing methods in the literature tailored to such tasks are mostly linear in nature;depending on the specific formulation, they largely rely on the assumption that the (latent) processes that govern the dynamics of the high- and low -frequency blocks of variables evolve at the same frequency, either the low or the high one. This paper develops a neural network -based multi -task shared -encoder -dual -decoder framework for joint multi -horizon prediction of both the low- and high -frequency blocks of variables, wherein the encoder/decoder modules can be either long shortterm memory or transformer ones. It addresses forecast/nowcast tasks in a unified manner, leveraging the encoder-decoder structure that can naturally accommodate the mixed -frequency nature of the data. The proposed framework exhibited competitive performance when assessed on both synthetic data experiments and two real datasets of US macroeconomic indicators and electricity data. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY -NC -ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Mixed-frequency data encoder-decoder LSTM Transformer Multi-horizon forecasts Nowcasting

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基于encoder-decoder-ILSTM模型的瓦斯浓度预测研究

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能源与节能 2023年第12期 102-105,176页

作者：陈小建山西新景矿煤业有限公司山西阳泉045000

近年来,神经网络在各领域均发挥了巨大作用,同样在煤矿瓦斯浓度预测当中也有应用。为了提高模型的预测精度和实时性,结合encoder-decoder结构、长短期记忆形成、蛇优化算法提出了一种新的神经网络,为促进煤矿安全生产提供了技术支持。

关键词：神经网络 encoder-decoder 蛇优化算法瓦斯浓度预测

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DHANet: An encoderdecoder Network With Multiscale Features Fusing for Optic Disk Segmentation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zheng, Xuan Jiang, Yanglin He, Yi Yuan, Huaqing Xu, Yanbin Du, Peng Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Techno Tianjin 300072 Tianjin Peoples R China Tianjin Eye Hosp Tianjin Key Lab Ophthalmol & Visual Sci Tianjin 300020 Peoples R China

Automatic and accurate segmentation of the optic disk (OD) region has practical applications in the medical field. In this study, a novel encoder-decoder network is proposed to segment the ODs automatically and accurately. The encoder consists of three parts: 1) low-level feature extraction module composed of a dense connectivity block (Dense Block) which can output rich low-level features;2) high-resolution block (HR Block) which can extract sufficient semantic information while reducing parameters;and 3) atrous spatial pyramid pooling (ASPP) module is used to obtain high-level features. Therefore, the network is named DHANet. The proposed decoder takes advantage of the multiscale features from the encoder to predict OD regions. By comparing it with existing classic models such as U-Net, CE-Net, and DeepLabv3+, as well as the latest excellent U-Net++, Attention U-Net, and CrackSegNet, it has been proven that the proposed method can generally achieve better segmentation performance at a lower cost. The ablation studies proved the influence of each module on the segmentation performance and explained the network structure reasonably. In the case of fewer network parameters, DHANet achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. DHANet is lightweight relatively and can use multiscale features to predict OD regions.

关键词： Training Image segmentation Data mining Artificial intelligence Optical imaging Mirrors Feature extraction Accuracy Sensitivity Retina Convolutional neural network encoder-decoder medical image segmentation multiscale features optic disk (OD)

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EPSegNet: Lightweight Semantic Recalibration and Assembly for Efficient Polyp Segmentation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Wu, Huisi Zhao, Zebin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Colorectal cancer (CRC) is among the most common malignancies and the detection and removal of polyps at the early stage is of great importance to prevent it. However, current state-of-the-art high-accuracy methods for polyps segmentation have a large number of parameters and a stringent requirement for computational cost, while lightweight and fast models significantly sacrifice accuracy. Currently, medical semantic segmentation algorithms are mostly based on encoder-decoder architecture. Pixelwise spatial information has been proven to be very important to the quality of features extracted by encoders. However, almost all existing approaches capturing it suffer from high computational complexity. Furthermore, the capacity of the traditional decoder is limited by its limited receptive fields. To comprehensively address the above problems, we propose a novel efficient polyp segmentation network (EPSegNet) to simultaneously fulfill the requirements of accuracy, size, and speed. First, we propose a lightweight feature extraction and recalibration module (LFERM), which can efficiently extract dense multiscale features. Specifically, in LFERM, we propose a spatial information recalibration (SIR) block for efficiently refining spatial information. Based on LFERMs, we develop an encoder. Moreover, we propose a novel lightweight semantic assembly decoder (LSAD) that assembles both channelwise and pixelwise semantics from a global context view. Finally, we combine the encoder and LSAD to form the proposed EPSegNet. Experiments on Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB datasets demonstrate that the proposed EPSegNet achieves the best balance between accuracy and size among state-of-the-art models and obtains a fast speed for polyp segmentation. Without any pretraining and postprocessing, our method achieves 79.37% intersection over union (IoU) and 86.74% Dice on the Kvasie-SEG dataset with only 0.34 million parameters and a speed of 128 frames/s (FPS) at the input size of

关键词： Feature extraction Semantics Accuracy Decoding Shape Data mining Semantic segmentation Colonoscopy Context modeling Computational modeling encoder-decoder lightweight polyp segmentation semantic assembly semantic recalibration

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EDSRNet: An Enhanced decoder Semantic Recovery Network for 2D Medical Image Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第2期29卷 1113-1124页

作者： Sun, Feng Zhou, Ying Hu, Longxiangfeng Li, Yongyan Zhao, Dan Chen, Yufeng He, Yu Univ Elect Sci & Technol China Mianyang Cent Hosp Sch Med Mianyang 621000 Peoples R China Mianyang Cent Hosp NHC Key Lab Nucl Technol Med Transformat Mianyang 621000 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621000 Peoples R China

In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treatment planning. Current deep learning-based medical image segmentation methods mainly adopt encoder-decoder architecture design and have received wide attention. However, these methods still have some limitations, including: (1) Existing methods are often influenced by the significant semantic information gap when supplementing features for the decoder. (2) Existing methods do not simultaneously consider global and local information interaction during decoding, resulting in ineffective semantic recovery. Therefore, this paper proposes a novel Enhanced decoder Semantic Recovery Network to address these challenges. Firstly, the Multi-Level Semantic Fusion (MLSF) module is introduced, which effectively fuses low-level features of the original image, encoder features, high-level features of the deepest network layer, and decoder features, and assigns weights based on semantic gaps. Secondly, the Multiscale Spatial Attention (MSSA) and Cross Convolution Channel Attention (CCCA) modules are employed to obtain richer feature information. Finally, the Global-Local Semantic Recovery (GLSR) module is designed to achieve better semantic recovery. Experiments on public datasets such as BUSI, CVC-ClinicDB, and Kvasir-SEG demonstrate that the proposed model improves IoU compared to suboptimal algorithms by 0.81%, 0.85% and 1.98%, respectively, significantly enhancing the performance of 2D medical image segmentation. This method provides effective technical support for further development in the field of medical image.

关键词： encoder-decoder multi-level semantic medical image segmentation semantic recovery encoder-decoder multi-level semantic medical image segmentation semantic recovery

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