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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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Improving the Efficiency of encoder-decoder architecture for Pixel-Level Crack Detection

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IEEE ACCESS 2019年 7卷 186657-186670页

作者： Chen, Hanshen Lin, Huiping Yao, Minghai Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Inst Commun Hangzhou 311112 Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Stomatol Hangzhou 310003 Peoples R China

Cracks are one of the most common categories of pavement distress that may potentially threaten road and highway safety. Thus, a reliable and efficient pixel-level method of crack detection is necessary for real-time measurement of the crack. However, many existing encoder-decoder architectures for crack detection are time-consuming because the part of decoder module always has lots of convolutional layers and feature channels that lead to performance that highly relies on computing resources, which is a handicap in scenarios with limited resources. In this study, we propose a simple and effective method to boost the algorithmic efficiency based on encoder-decoder architecture for crack detection. We develop a switch module, called SWM, to predict whether the image is positive or negative and then skip the decoder module to save computation time when it is negative. This method uses the encoder module as the fixed feature extractor and only needs to place a light-weight classifier head on the end of the encoder module to output the final class probability. We choose the classical UNet and DeepCrack as examples of the encoder-decoder architectures to show how SWM is integrated into the architectures to reduce computation complexity. Evaluations on the public CrackTree206 and AIMCrack datasets demonstrate that our method can significantly boost the efficiency of the encoder-decoder architectures in all tasks, while without affecting the performance. The SWM can also be easily embedded into other encoder-decoder architectures for further improvement. The source code is available at https://***/hanshenchen/crack-detection.

关键词： Computer vision deep learning encoder-decoder architecture 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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Rethinking the encoder-decoder Structure in Medical Image Segmentation from Releasing decoder Structure

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Journal of Bionic Engineering 2024年第3期21卷 1511-1521页

作者： Jiajia Ni Wei Mu An Pan Zhengming Chen School of Artificial Intelligence Anhui Polytechnic UniversityWuhu241000China School of Information Science and Engineering HoHai UniversityChangzhou213000China

Medical image segmentation has witnessed rapid advancements with the emergence of encoder-decoder based *** the encoder-decoder structure,the primary goal of the decoding phase is not only to restore feature map resolution,but also to mitigate the loss of feature information incurred during the encoding ***,this approach gives rise to a challenge:multiple up-sampling operations in the decoder segment result in the loss of feature *** address this challenge,we propose a novel network that removes the decoding structure to reduce feature information loss(CBL-Net).In particular,we introduce a Parallel Pooling Module(PPM)to counteract the feature information loss stemming from conventional and pooling operations during the encoding ***,we incorporate a Multiplexed Dilation Convolution(MDC)module to expand the network's receptive ***,although we have removed the decoding stage,we still need to recover the feature map ***,we introduced the Global Feature Recovery(GFR)*** uses attention mechanism for the image feature map resolution recovery,which can effectively reduce the loss of feature *** conduct extensive experimental evaluations on three publicly available medical image segmentation datasets:DRIVE,CHASEDB and MoNuSeg *** results show that our proposed network outperforms state-of-the-art methods in medical image *** addition,it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.

关键词： Medical image segmentation encoder-decoder architecture Attention mechanisms Releasing decoder architecture Neural network

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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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A Robust Light-Weight Fused-Feature encoder-decoder Model for Monocular Facial Depth Estimation From Single Images Trained on Synthetic Data

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IEEE ACCESS 2023年 11卷 41480-41491页

作者： Khan, Faisal Shariff, Waseem Farooq, Muhammad Ali Basak, Shubhajit Corcoran, Peter Natl Univ Ireland Galway NUIG Sch Engn Galway H91 TK33 Ireland Natl Univ Ireland Galway NUIG Sch Comp Sci Galway H91 TK33 Ireland Xperi Inc Galway H91V0TX Ireland

Due to the real-time acquisition and reasonable cost of consumer cameras, monocular depth maps have been employed in a variety of visual applications. Regarding ongoing research in depth estimation, they continue to suffer from low accuracy and enormous sensor noise. To improve the prediction of depth maps, this paper proposed a lightweight neural facial depth estimation model based on single image frames. Following a basic encoder-decoder network design, the features are extracted by initializing the encoder with a high-performance pre-trained network and reconstructing high-quality facial depth maps with a simple decoder. The model can employ pixel representations and recover full details in terms of facial features and boundaries by employing a feature fusion module. When tested and evaluated across four public facial depth datasets, the suggested network provides more reliable and state-of-the-art results, with significantly less computational complexity and a reduced number of parameters. The training procedure is primarily based on the use of synthetic human facial images, which provide a consistent ground truth depth map, and the employment of an appropriate loss function leads to higher performance. Numerous experiments have been performed to validate and demonstrate the usefulness of the proposed approach. Finally, the model performs better than existing comparative facial depth networks in terms of generalization ability and robustness across different test datasets, setting a new baseline method for facial depth maps.

关键词： Estimation Facial recognition Three-dimensional displays Computational modeling Encoding Feature extraction Cameras Deep learning Facial depth estimation feature fusion encoder-decoder architecture deep learning

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DGCN-ED: dynamic graph convolutional networks with encoder-decoder structure and its application for airborne LiDAR point classification

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第11期44卷 3489-3506页

作者： Chen, Yang Xing, Yin Li, Xiao Gao, Wei Suzhou Inst Trade & Commerce Sch Informat Technol Suzhou Peoples R China Suzhou Univ Sci & Technol Sch Geog Sci & Geomat Engn Suzhou 215009 Peoples R China Suzhou LJT Intelligent Technol Co Ltd Adm & Enterprise Planning Ctr Suzhou Peoples R China

Achieving high-precision automatic classification in real-world applications for airborne laser scanning point clouds is a challenging task duo to their unstructured nature, uneven density distribution, high redundancy, incompleteness and scene complexity. Graph Convolutional Neural Networks can process scattered point clouds directly without regularization, which avoids the loss of depth information?and has recently become a topic of increased interest. Therefore, this study proposed an extension of the Graph-Unet network architecture named DGCN-ED for airborne LiDAR point classification, which uses a Graph Convolutional Neural Network as a representation to describe complex object relationships and an encoder-decoder architecture to capture the multi-scaled point features and describe objects in the high-level feature space. The two-layer dynamic update Graph Convolutional Neural Network is designed to expand the effective range of nodes and enhance the representation ability of learned pointwise features. The effectiveness of the proposed method is evaluated by an experiment on the ISPRS Vaihingen 3D semantic labelling benchmark dataset. Moreover, experiments on the IEEE 2019 Data Fusion Contest Dataset were conducted to demonstrate the generalization abilities of the proposed method. The results show that our method achieved on average 2.8% higher overall accuracy than existing methods, with an overall accuracy of 98.0% and an average F1 score of 0.797.

关键词： Airborne LiDAR point classification dynamic graph convolutional network encoder-decoder architecture high-level information collection

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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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