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Learning and Adaptation From Minimum Samples With Heterogeneous Quality: An Investigation of Image Segmentation networks on Natural Dataset

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IEEE ACCESS 2023年 11卷 47040-47052页

作者： Variyar, V. V. Sajith Sowmya, V. Sivanpillai, Ramesh Brown, Gregory K. Amrita Vishwa Vidyapeetham Ctr Computat Engn & Networking CEN Coimbatore 641112 India Univ Wyoming Wyoming GIS Ctr Sch Comp Laramie WY 82071 USA Univ Wyoming Dept Bot Laramie WY 82071 USA

Training deep learning-based image segmentation networks require large number of samples of adequate quality. However, obtaining large number of samples is not possible in certain domains. Recent approaches use augmentation and transfer learning techniques to overcome small sample size. Augmentation techniques are known to introduce noise to the dataset, while transfer learning approaches may fail if the existing dataset is novel to deep learning algorithms. This study investigated how four deep learning-based image segmentation networks learned and adapted to identify epiphytes when trained with fewer image samples (n = 132) of heterogeneous quality without transfer learning and data augmentation. encoder-decoder with skip connection (Unet), Deep Residual (DRUnet), Vision transformer (TransUnet), and Conditional Generative (Pix2Pix) represent different generations of deep learning networks. The segmentation performance of the trained models was evaluated by computing the Jaccard score (IoU) for predicted labels for test images. Test images (n = 20) with heterogeneous quality were evaluated by categorizing them into six categories based on target occupancy and lighting conditions. Results from this study showed that among the four networks, predicted images from the TransUnet model achieved high average Jaccard score of 0.78. Role of additional layers apart from Unet was important for accurate localization and context understanding of the target plants. However, these networks misclassified visually similar plants as target plant. The transformer and attention layers in TransUnet showed significant contribution towards improvement in localizing target and understanding context in images with varying quality. TransUnet can be used for segmenting target plants when fewer training samples are available. The presence of Unet based encoder-decoder in TransUnet is well contributing for deriving good features from minimum samples.

关键词： Training Image segmentation Deep learning Transformers Transfer learning Decoding Location awareness less training samples image quality INDEX TERMS encoder-decoder networks deep neural networks vision transformer

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Deep capsule encoder-decoder network for surrogate modeling and uncertainty quantification

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2023年第12期124卷 2783-2800页

作者： Thakur, Akshay Chakraborty, Souvik Indian Inst Technol Delhi Dept Appl Mech Hauz Khas New Delhi India Indian Inst Technol Delhi Sch Artificial Intelligence Hauz Khas New Delhi India Indian Inst Technol Delhi Dept Appl Mech Hauz Khas New Delhi 110016 India

We propose a novel capsule-based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data. The proposed framework is developed by adapting Capsule Network (CapsNet) architecture into an image-to-image regression encoder-decoder network. Specifically, the aim is to exploit the benefits of CapsNet over convolution neural network (CNN) - retaining pose and position information related to an entity to name a few. The performance of the proposed approach is illustrated by solving two different variants of elliptic partial differential equations (PDE): a stochastic version without a source term having an input dimensionality of 1024 and a deterministic pressure Poisson equation for flow past a cylinder. The first PDE, that is, the stochastic PDE (SPDE), also governs systems in mechanics such as steady heat conduction, groundwater flow, or other diffusion processes. In this article, the problem definition for this SPDE is such that it does not restrict the random diffusion field to a particular covariance structure, and the more strenuous task of response prediction for an arbitrary diffusion field is solved. Finally, we also evaluate the performance of our model on the uncertainty propagation problem based on this equation. The obtained results from the performance evaluation of the developed model on the mentioned problems indicate that the proposed approach is accurate, data efficient, and robust.

关键词： capsule network deep learning elliptic stochastic PDE encoder-decoder networks uncertainty quantification

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Contextual ensemble network for semantic segmentation

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PATTERN RECOGNITION 2022年 122卷 108290-108290页

作者： Zhou, Quan Wu, Xiaofu Zhang, Suofei Kang, Bin Ge, Zongyuan Latecki, Longin Jan Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Networking Nanjing 21003 Peoples R China Nanjing Univ Posts & Telecommun Dept Internet Things Nanjing 21003 Peoples R China Monash Univ Monash eRes Ctr Melbourne Vic Australia Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Recently, exploring features from different layers in fully convolutional networks (FCNs) has gained sub-stantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called contextual ensemble network (CENet), for semantic segmentation, where the contextual cues are aggregated via densely usampling the convolutional features of deep layer to the shallow deconvolutional layers. The proposed CENet is trained in terms of end-to-end segmenta-tion to match the resolution of input image, and allows us to fully explore contextual features through ensemble of dense deconvolutions. We evaluate our CENet on two widely-used semantic segmentation datasets: PASCAL VOC 2012 and CityScapes. The experimental results demonstrate our CENet achieves superior performance with respect to recent state-of-the-art results. Furthermore, we also evaluate CENet on MS COCO dataset and ISBI 2012 dataset for the task of instance segmentation and biological segmen-tation, respectively. The experimental results show that CENet obtains promising results on these two datasets. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Ensemble deconvolution Semantic segmentation FCNs Context aggregation encoder-decoder networks

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Unpaired Image Captioning With semantic-Constrained Self-Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 904-916页

作者： Ben, Huixia Pan, Yingwei Li, Yehao Yao, Ting Hong, Richang Wang, Meng Mei, Tao Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China JD AI Res CV Lab Beijing 100105 Peoples R China

Image captioning has been an emerging and fast-developing research topic. Nevertheless, most existing works heavily rely on large amounts of image-sentence pairs and therefore hinder the practical applications of captioning in the wild. In this paper, we present a novel Semantic-Constrained Self-learning (SCS) framework that explores an iterative self-learning strategy to learn an image captioner with only unpaired image and text data. Technically, SCS consists of two stages, i.e., pseudo pair generation and captioner re-training, iteratively producing "pseudo" image-sentence pairs via a pre-trained captioner and re-training the captioner with the pseudo pairs, respectively. Particularly, both stages are guided by the recognized objects in the image, that act as semantic constraint to strengthen the semantic alignment between the input image and the output sentence. We leverage a semantic-constrained beam search for pseudo pair generation to regularize the decoding process with the recognized objects via forcing the inclusion/exclusion of the recognized/irrelevant objects in output sentence. For captioner re-training, a self-supervised triplet loss is utilized to preserve the relative semantic similarity ordering among generated sentences with regard to the input image triplets. Moreover, an object inclusion reward and an adversarial reward are adopted to encourage the inclusion of the predicted objects in the output sentence and pursue the generation of more realistic sentences during self-critical training, respectively. Experiments conducted on both dependent and independent unpaired data validate the superiority of SCS. More remarkably, we obtain the best published CIDEr score to-date of 74.7\% on COCO Karpathy test split for unpaired image captioning.

关键词： Semantics Image recognition Training Visualization Decoding Task analysis Dogs encoder-decoder networks image captioning self-supervised learning

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Learning Geometric Features for Improving the Automatic Detection of Citrus Plantation Rows in UAV Images

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

作者： Cue La Rosa, Laura Elena Oliveira, Dario A. B. Zortea, Maciel Gemignani, Bruno Holtz Feitosa, Raul Queiroz IBM Res BR-22290240 Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn BR-22451900 Rio De Janeiro Brazil 3DGEO BR-80320200 Curitiba Parana Brazil

Unmanned aerial vehicles (UAVs) allow on-demand imaging of orchards at an unprecedented level of detail. The automated detection of plantation rows in the images helps in the successive analysis steps, such as the detection of individual fruit trees and planting gaps, aiding producers with inventory and planting operations. Citrus trees can be planted in curved rows that form intricate geometric patterns in aerial images, requiring robust detection approaches. While deep learning methods rank among state-of-the-art methods for segmenting images with particular geometrical patterns, they struggle to hold their performance when testing data differs much from training data (e.g., image intensity differences, image artifacts, vegetation characteristics, and landscape conditions). In this letter, we propose a method to learn geometric features of orchards in UAV images and use them to improve the detection of plantation rows. First, we train a detection encoder-decoder network (DetED) to segment planting rows in RGB images. Then, with labeled data, we train an encoder-decoder correction network (CorrED) that learns to map binary masks with spurious row segmentation geometries into corrected ones. Finally, we use the CorrED network to fix geometric inconsistencies in DetED outcome. Our experiments with commercial plantations of orange trees show that the proposed CorrED postprocessing can restore missing segments of plantation rows and improve detection accuracy in testing data.

关键词： Image segmentation Geometry Vegetation Training Testing Decoding Feature extraction encoder-decoder networks geometric patterns plantation rows detection postprocessing

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Uni-EDEN: Universal encoder-decoder Network by Multi-Granular Vision-Language Pre-training

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2022年第2期18卷 1-16页

作者： Li, Yehao Fan, Jiahao Pan, Yingwei Yao, Ting Lin, Weiyao Mei, Tao JD AI Res 8 Beichen West St Beijing 100105 Peoples R China Shanghai Jiao Tong Univ 800 Dongchuan Rd Shanghai 200240 Peoples R China

Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource downstream tasks. Unlike existing works that predominantly learn a single generic encoder, we present a pre-trainable Universal encoder-decoder Network (Uni-EDEN) to facilitate both vision-language perception (e.g., visual question answering) and generation (e.g., image captioning). Uni-EDEN is a two-stream Transformer-based structure, consisting of three modules: object and sentence encoders that separately learns the representations of each modality and sentence decoder that enables both multi-modal reasoning and sentence generation via inter-modal interaction. Considering that the linguistic representations of each image can span different granularities in this hierarchy including, from simple to comprehensive, individual label, a phrase, and a natural sentence, we pre-train Uni-EDEN through multi-granular vision-language proxy tasks: Masked Object Classification, Masked Region Phrase Generation, Image-Sentence Matching, and Masked Sentence Generation. In this way, Uni-EDEN is endowed with the power of both multi-modal representation extraction and language modeling. Extensive experiments demonstrate the compelling generalizability of Uni-EDEN by fine-tuning it to four vision-language perception and generation downstream tasks.

关键词： Vision-language pre-training encoder-decoder networks

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Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 73-95页

作者： Jia Chen Zhiqiang He Dayong Zhu Bei Hui Rita Yi Man Li Xiao-Guang Yue School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Economics and Finance/Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHongkongChina Rattanakosin International College of Creative Entrepreneurship Rajamangala University of Technology RattanakosinNakhon Pathom73170Thailand Department of Computer Science and Engineering School of SciencesEuropean University CyprusNicosia1516Cyprus CIICESI ESTGPolit′ecnico do Porto4610-156FelgueirasPortugal

Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment *** 2015,U-Net-based approaches have been widely used formedical image *** purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature ***,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level *** high-level information can make the segmentationmore *** this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level *** MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive *** proposed MU-Net architecture is evaluated based on three different medical imaging *** experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different *** the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.

关键词： Medical image segmentation MU-Net(multi-path upsampling convolution network) U-Net clinical diagnosis encoder-decoder networks

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Anchor Ball Regression Model for large-scale 3D skull landmark detection

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NEUROCOMPUTING 2024年 567卷

作者： He, Tao Xu, Guikun Cui, Li Tang, Wei Long, Jie Guo, Jixiang Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Peoples R China West China Hosp Stomatol Dept Oral & Maxillofacial Surg Chengdu 610041 Peoples R China Army Med Univ Daping Hosp Dept Oral & Maxillofacial Surg Chongqing 400042 Peoples R China

Recent deep learning models have exhibited impressive performance in the area of 3D skull landmark detection, but most of them aimed to detect a fixed number of landmarks. This paper focuses on automatically detecting an arbitrary number of landmarks on CT volumes, which meets the real clinical needs. To achieve robust performance for detecting arbitrary molar landmarks, we propose a novel 3D landmark detection model named Anchor Ball Regression Model (ABRM), which combines landmark detection and landmark classification losses for network training. For landmark detection, a novel landmark regression loss is proposed by predicting offsets to anchor balls instead of directly predicting landmarks. For landmark classification, an online hard negative mining loss is used to reduce absent landmarks' learning errors, and a small regularization constraint loss is performed for voxels outside the anchor balls. The network backbone of ABRM is obtained by manually pruning popular 3D-CNNs. We also present an available large-scale benchmark dataset in this paper, which, to the best of our knowledge, is the largest dataset for 3D skull landmark detection. The dataset comprises of 658 CT volumes, with 14 landmarks labeled by two junior and one senior doctors. The ABRM presents a good robust performance and outperforms other models on this dataset. The codes and dataset are accessible at https://***/ithet1007/mmld_code.

关键词： encoder-decoder networks 3D skull landmark detection CT volumes

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INet: Convolutional networks for Biomedical Image Segmentation

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IEEE ACCESS 2021年 9卷 16591-16603页

作者： Weng, Weihao Zhu, Xin Univ Aizu Biomed Informat Engn Lab Aizu Wakamatsu Fukushima 9658580 Japan

encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because down- and upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from 3x3 to 7x7 and then 15x15 ) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder-decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling t

关键词： Biomedical image convolutional networks encoder-decoder networks semantic segmentation

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DCANet: Dual contextual affinity network for mass segmentation in whole mammograms

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MEDICAL PHYSICS 2021年第8期48卷 4291-4303页

作者： Lou, Meng Qi, Yunliang Meng, Jie Xu, Chunbo Wang, Yiming Pi, Jiande Ma, Yide Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

Purpose Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer-aided diagnosis systems. Existing algorithms mainly used mass-centered patches to achieve mass segmentation, which is time-consuming and unstable in clinical diagnosis. Therefore, we aim to directly perform fully automated mass segmentation in whole mammograms with deep learning solutions. Methods In this work, we propose a novel dual contextual affinity network (a.k.a., DCANet) for mass segmentation in whole mammograms. Based on the encoder-decoder structure, two lightweight yet effective contextual affinity modules including the global-guided affinity module (GAM) and the local-guided affinity module (LAM) are proposed. The former aggregates the features integrated by all positions and captures long-range contextual dependencies, aiming to enhance the feature representations of homogeneous regions. The latter emphasizes semantic information around each position and exploits contextual affinity based on the local field-of-view, aiming to improve the indistinction among heterogeneous regions. Results The proposed DCANet is greatly demonstrated on two public mammographic databases including the DDSM and the INbreast, achieving the Dice similarity coefficient (DSC) of 85.95% and 84.65%, respectively. Both segmentation performance and computational efficiency outperform the current state-of-the-art methods. Conclusion According to extensive qualitative and quantitative analyses, we believe that the proposed fully automated approach has sufficient robustness to provide fast and accurate diagnoses for possible clinical breast mass segmentation.

关键词： whole mammograms mass segmentation deep learning encoder-decoder networks contextual affinity

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