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Automatic Colorectal Polyp Segmentation Using Convolutional Neural Networks Based on the encoder-decoder architecture with new design Skip Connections

Automatic Colorectal Polyp Segmentation Using Convolutional ...

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第22届中国系统仿真技术及其应用学术年会（CCSSTA22nd 2021）

作者： Foroughi Farzin Chen Zonghai Department of Automation University of Science and Technology of China

A challenging task in medical dermoscopic image segmentation is detecting the location of the *** detection of a polyp is even more difficult owing to its low contrast with the surrounding *** paper proposed an end-to-end network based on the convolutional neural network,which does not require any prior processing to detect and segment the polyps in the medical *** proposed convolutional neural network is based on the encoder-decoder architecture,which advantages the benefits of skip *** connections can play an effective role in the encoder-decoder-based networks but have not received enough attention in network *** advantage of the proposed model is that it has a unique skip connection design that leads to better accuracy in the ***,the proposed model is tested on the CVC-ClinicDB dataset,and the experimental results illustrate our model outperforms other state-of-the-art approaches.

关键词： Deep Learning Convolutional Neural Network encoder-decoder architecture Medical Image Segmentation Skip Connections

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Crack-SegNet: Surface Crack Detection in Complex Background Using encoder-decoder architecture 2021

Crack-SegNet: Surface Crack Detection in Complex Background ...

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2021 4th International Conference on Sensors, Signal and Image Processing

作者： Rong Ran Xinghua Xu Shaohua Qiu Xiaopeng Cui Fuhui Wu National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering China

ISBN: (纸本)9781450385725

Timely and accurate detection of the initiation and expansion of crack is of great significance for improving safe operation of civil infrastructures. Image-based visual surface inspection has been an indispensable way for long-time infrastructure monitoring. However, existing crack detection methods generally suffer from the interference of complex background, leading to obvious performance drops. To tackle this, an improved encoder-decoder architecture based on SegNet is proposed in this paper, namely crack-SegNet. The encoder network hierarchically learns visual features from the original image, and the decoder network gradually up-samples and maps the encoded features to the input size for the pixel-level classification. In order to enhance the feature capacity of cracks in complex background, a channel attention mechanism is integrated into the encoder, as well as a spatial attention module in the decoder to improve the feature representation of cracks. Meanwhile, a spatial pyramid pooling is also attached to the last convolutional layer of the encoder to capture crack with different scales. To better validate the proposed method, a challenging metal surface crack dataset with much more complex background is collected. Experimental results on the datasets show that the proposed crack-SegNet outperforms other state-of-the-art crack detection methods, especially in complex background.

关键词： encoder-decoder architecture Complex background Semantic segmentation Deep convolutional neural works Surface crack detection

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Automated pancreatic mass segmentation in computer tomography images using a voting ensemble method based on encoder-decoder architectures

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Procedia Computer Science 2025年 256卷 1167-1174页

作者： Alexandre de Carvalho Araújo João Dallyson Sousa de Almeida Anselmo Cardoso de Paiva Geraldo Braz Júnior ºNúcleo de Computação Aplicada – Federal University of Maranhãio (UFMA) São Luis MA Brasil

Pancreatic cancer poses significant challenges in early diagnosis, with a high mortality rate of 98%, and is responsible for 4.7% of cancer deaths. Early diagnosis, mainly done by imaging exams, is the main factor that determines prognosis. While it is common to perform cascaded segmentation, the use of ensemble strategies is rarely explored in the literature for pancreatic mass segmentation. In cascaded methods, the performance of the pancreatic mass segmentation step is also directly affected by the previous steps. In this paper we aim to study the impact of a localization step in three levels of precision while using an ensemble method to carry out pancreatic mass segmentation. A voting ensemble method is proposed that combine three encoder-decoder based networks, namely U-Net, Feature Pyramid Network (FPN) and LinkNet. The results obtained were competitive with existing literature, achieving a Dice Score of 63.89 ± 2.88% on the smallest resolution and 60.35 ± 4.86% on the biggest resolution on the MSD dataset. The results show that an ensemble method can mitigate the impact of previous steps in a cascaded method without significant loss in performance.

关键词： Pancreatic mass Ensemble Model encoder-decoder architecture

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Assessing the Impact of Rainfall Nowcasts on an encoder-decoder LSTM Model for Short-Term Flash Flood Prediction

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WATER RESOURCES MANAGEMENT 2025年第4期39卷 1623-1638页

作者： Mhedhbi, Rim Erechtchoukova, Marina G. York Univ Sch Informat Technol Fac Liberal Arts & Profess Studies 4700 Keele St Toronto ON M3J 1P3 Canada

Flash floods pose significant threats as immediate and highly destructive natural hazards. Extending the forecast horizon of flash flood prediction models has been a key objective to enable timely warning or other mitigating measures. The integration of precipitation predictions into data-driven flash flood models remains unexplored. In this study, we propose an encoder-decoder LSTM-based model architecture for short-term flash flood prediction, which incorporates short-term rainfall forecasts and evaluates the influence of the associated uncertainty on these predictions. We conducted three sets of experiments to predict flash flood occurrences within a watershed with a 30-minute response time. The first set employed a baseline LSTM model without rainfall forecast integration. The second one utilized a proposed encoder-decoder LSTM model that incorporated accurate rainfall forecasts. Lastly, the third set of experiments introduced errors into the rainfall forecasts to evaluate the impact of forecast uncertainty on flood prediction. Computational experiments demonstrate that incorporating accurate rainfall nowcasts significantly enhances flash flood predictability, with F1-score improvements ranging from 10 to 60%, depending on the hydrological year. Furthermore, even when errors in rainfall magnitude and timing were introduced, overall the proposed framework outperformed models that did not use rainfall forecasts, delivering reliable predictions for up to two hours.

关键词： LSTM Model encoder-decoder architecture Flash Flood Prediction Rainfall Nowcasting Forecast Uncertainty

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Human Trajectory Imputation Model: A Hybrid Deep Learning Approach for Pedestrian Trajectory Imputation

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APPLIED SCIENCES-BASEL 2025年第2期15卷 745-745页

作者： Barua, Deb Kanti Halder, Mithun Shopnil, Shayanta Islam, Md. Motaharul United Int Univ Dept Comp Sci & Engn Madani Ave Dhaka 1212 Bangladesh

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges in adverse weather conditions, interference from other vehicles' sensors and electronic devices, and signal reception failure, leading to incompleteness in the trajectory data. But for real-time decision making for autonomous driving, trajectory imputation is no less crucial. Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, the Human Trajectory Imputation Model, coined HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model is evaluated using the Intersection drone the inD dataset, containing trajectory data at suitable altitudes, preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we utilize L1, MSE, and quantile and ADE loss. Our experiments demonstrate that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

关键词： human trajectory autonomous vehicle encoder-decoder architecture deep learning inD dataset

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DenSFA-PU: Learning to unwrap phase in severe noisy conditions

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OPTICS AND LASER TECHNOLOGY 2025年 187卷

作者： Awais, Muhammad Yoon, Taeil Hwang, Chi-Ok Lee, Byeongha Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea Korea Univ Dept Phys Seoul 02855 South Korea Gwangju Inst Sci & Technol Nat Sci Coll Dept Math Sci Gwangju 61005 South Korea

In optics, phase measurement techniques face challenges because phase values are confined within 2it, leading to the problem of phase unwrapping. Many methods, including deep learning-based approaches, have been proposed to address this issue. However, high noise in a wrapped phase image often causes these techniques to fail, resulting in error accumulation and high computation time. To overcome these challenges, we propose a robust and fast deep learning-based method called DenSFA-PU (Densely Connected Spatial Feature Aggregator for Phase Unwrapping), which treats this problem as a regression task. Our approach uses an encoder-decoder architecture with densely connected neural networks and a spatial feature aggregator module for noise reduction and robust feature representation. Comparative analysis using both synthetic data and real-world data obtained through digital holography demonstrates that our method outperforms existing techniques, achieving greater computational efficiency with an average unwrapping time of 29.31 ms, significantly faster than other methods. It also shows superior accuracy, with consistently good NRMSE, PSNR, and SSIM values across all cases, highlighting its robustness in handling highly noisy wrapped phase images. Additionally, its ability to operate with minimal training data makes it highly suitable for the applications requiring fast and accurate phase unwrapping with a limited data set.

关键词： Phase unwrapping Digital holography Deep learning Densely connected neural networks encoder-decoder architecture Spatial feature aggregator

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Enhancing multi-step-ahead algal bloom forecasts in river ecosystems by a hybrid recursive deep learning model

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HYDROLOGY RESEARCH 2025年第3期56卷 260-278页

作者： Xu, Hanbing Wang, Xue Zhou, Yanlai Xia, Tianyu Chang, Fi-John Xu, Chong-Yu Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Changjiang Water Resources Commiss River & Lake Protect & Construction Safety Operat Wuhan 430010 Peoples R China Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10617 Taiwan Univ Oslo Dept Geosci POB1047 Blindern N-0316 Oslo Norway

River algal blooms pose a significant environmental threat, necessitating accurate forecasts and timely warnings for effective prevention. This study proposes a novel hybrid model, combining an external recursive long short-term memory neural network based on encoder-decoder (RLSTM-ED) with a backpropagation (BP) neural network, denoted as RLSTM-ED-BP. A dataset comprising 34,992 hydrological, climatic, and water quality (4-hourly) observations from the Hanjiang River Basin in China was divided for model training and testing. Comparative analysis with an RLSTM baseline demonstrated that the RLSTM-ED-BP model enhanced the Nash-Sutcliffe coefficient (NSE) by more than 5% and reduced the root mean square error by over 10% during the 24-h forecast horizon. The RLSTM-ED-BP model yielded NSE and threat score values exceeding 0.95 and efficiently provided early warnings for algal bloom events. The model's enhanced performance contributes to the generalizability of deep learning approaches in addressing the critical environmental challenge of algal blooms.

关键词： algal bloom prediction early warning encoder-decoder architecture Hanjiang River Basin recursive strategy

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A graph attention-based policy gradient method with an adaptive embedding strategy for k-center problems

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APPLIED SOFT COMPUTING 2025年 173卷

作者： Zhao, Zhonghao Lee, Carman K. M. Yan, Xiaoyuan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

The k-center problem (KCP) is a well-known NP-hard combinatorial optimization challenge in the field of computer science and operations research, aiming to determine optimal locations for k centers within a given set of nodes to minimize the maximum distance from each node to its nearest center. In contrast to conventional algorithms that have inherent limitations in handling the trade-off between solution quality and computational efficiency, this study proposes a new method based on a graph attention mechanism with an encoder-decoder architecture to find high-quality solutions for KCPs by directly learning heuristics from the graph. Specifically, the encoder processes the input feature of the graph and capture intricate spatial patterns and dependencies among nodes, whereas the decoder leverages the encoded information and attention weights to iteratively generate solutions for the KCP. Moreover, an adaptive embedding strategy is developed to handle the specific attributes and constraints inherent in different KCP instances. To find high-quality solutions, a policy gradient method with an exponential moving average baseline is developed to update and learn the optimal model parameters. A comprehensive set of experiments on multiple problem sizes are conducted to systematically compared the performance of the proposed method with a wide range of baseline methods across four types of KCPs, including the standard KCP, capacitated KCP, non-uniform KCP, and dynamic KCP. The experimental results demonstrate the competitive performance of the graph attention-based method in addressing KCPs.

关键词： Graph attention K -center problem encoder-decoder architecture Policy gradient method

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Innovative multistep and synchronous soft sensing prediction of COD and NH3 in WWTPs via multimodal data and multiple attention mechanisms

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WATER RESEARCH 2025年 278卷 123405页

作者： Li, Junchen Lin, Sijie Zhang, Liang Zhong, Lijin Ding, Longzhen Hu, Qing Harbin Inst Technol Sch Environm Harbin 150090 Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Engn Innovat Ctr SUSTech Beijing Beijing 100083 Peoples R China Beijing Univ Technol Fac Environm & Life Beijing 100124 Peoples R China Minist Educ China Engn Res Ctr Intelligence Percept & Autonomous Con Beijing 100124 Peoples R China

Accurate prediction of Chemical Oxygen Demand (COD) and ammonia nitrogen (NH3) is crucial for maintaining stable and effective wastewater treatment processes. Traditional methods rely on costly, high-maintenance sensors, limiting their application in resource-limited wastewater treatment plants. Soft sensing methods provide an alternative by reducing dependence on costly sensors. However, existing approaches cannot perform multitarget and multistep predictions, limiting their practical applicability. This study introduced a novel triple attention-enhanced encoder-decoder temporal convolutional network (TAED-TCN) to address this problem. The model used multimodal inputs, including easily accessible water quality parameters and wastewater surface images, for multistep and synchronous prediction of COD and NH3. When it was validated with real-world sequencing batch reactor wastewater data, the model demonstrated superior multistep prediction performance. Specifically, the R2 for 1-h predictions of COD and NH3 was over 26.03 % and 20.51 % higher than the baseline model, respectively. By incorporating multiple attention mechanisms (feature, temporal, and crossattention), TAED-TCN effectively captured essential features, model nonlinear relationships, and identified long-term dependencies, thus enabled consistent multitarget prediction results even under abnormal conditions. Additionally, economic analysis revealed that TAED-TCN could reduce COD and NH3 monitoring costs by 79 % over the equipment life cycle. This study offers a cost-effective solution for water quality prediction, enhancing the operational efficiency of wastewater management.

关键词： Water quality prediction Soft sensing Multiple attention mechanism encoder-decoder architecture Multimodal data

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ST-3DView: Multi-Scale Contrast-Enhanced 3D Point Cloud Reconstruction of Single-View Objects From Video Scene Transition

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IEEE ACCESS 2025年 13卷 69596-69618页

作者： Chakraborty, Dipanita Chiracharit, Werapon Chamnongthai, Kosin King Mongkuts Univ Technol Thonburi Dept Elect & Telecommun Engn Bangkok 10140 Thailand

3D object tracking in monocular video relies on understanding the scene content to improve the continuity of the tracking signal. Reconstructing 3D shapes of single-view objects is essential for capturing object depth, orientation, and position within the scene. While existing deep learning-based methods excel in 3D reconstruction and tracking, they primarily focus on object feature semantics in normal frames, neglecting scene transition (ST) frames. This limitation leads to object information loss and discontinuity during tracking. This paper proposes a novel method for 3D reconstruction of single-view objects in monocular video scenes, focusing on fade scene transitions. First, large video datasets are pre-processed and segmented into sequences using cut transition detection via adaptive histogram equalization (AHE), and Euclidean distance estimation (EDE). Second, fade transition sequences are detected and classified into fade-in, fade-out, and mixed-fade scene transitions using pixel intensity-based adaptive threshold. Third, contrast enhancement is applied to fade transition frames using contrast-limited adaptive histogram equalization (CLAHE) to improve object feature extraction. Fourth, a modified DeepLabv3+ network is employed to generate multi-scale features for semantic foreground object and background segmentation. Finally, the segmented objects are processed through the proposed Point-wise multilayer perceptron (MLP) network, which reconstructs 3D object point clouds from segmented 2D single-view object pixels. Experimental evaluations on object categories "Chair," "Car," and "Airplane" from the benchmark TRECVID, Pix3D, ShapeNet, and Multimedia datasets achieved an accuracy improvement of 6.52% for fade transition detection and satisfactory results in 3D point cloud reconstruction.

关键词： 3D neural network 3D reconstruction encoder-decoder architecture encoder-decoder architecture object segmentation object segmentation point cloud point cloud scene transition detection scene transition detection shot boundary detection shot boundary detection video scene content understanding video scene content understanding video scene content understanding

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