Encoder-decoder CNN Research Articles

InfPose: Real-Time Infrared Multi-Human Pose Estimation for Edge Devices Based on Encoder-Decoder CNN Architecture

InfPose: Real-Time Infrared Multi-Human Pose Estimation for Edge Devices Based on Encoder-Decoder CNN Architecture

Big data clustering through fusion of FCM, optimized encoder-decoder CNN, and BiLSTM

Clustering Big Data, as a fundamental component in the processing and analysis of massive datasets, holds crucial importance in addressing complex challenges inherent in handling extensive data sets. Falling within the realm of unsupervised learning methods, the primary objective of clustering is to efficiently organize substantial datasets into homogeneous clusters without relying on pre-existing labels. Our innovative approach seeks to optimize this process by synergistically combining three techniques: the fuzzy C-Means (FCM) methodology, the optimized encoder–decoder CNN model, and the bidirectional recurrent neural network (BiLSTM). This synergy represents a strategic convergence between supervised and unsupervised paradigms. The introduction of BiLSTM is of significant importance, leveraging its capability to sequentially process data from both sides using LSTM cells. This bidirectional approach enhances the understanding of data sequences, a crucial feature in the demanding context of Big Data clustering. Simultaneously, FCM benefits from substantial improvement through the introduction of a function that calculates the separation between the cluster center and the instance, thereby reinforcing the precision of clustering. To optimize performance and reduce computation time, our methodology advocates for the use of the Optimized Encoder–Decoder CNN model. This refined architecture promotes more efficient extraction of data features, thereby enhancing the intrinsic quality of clustering. The rigorous evaluation of our approach revolves around specific data sources, namely fashion MNIST. Performance criteria such as accuracy, adjusted rand index (ARI), and normalized mutual information (NMI) convincingly attest to the remarkable capability of our methodology. In comparative analyses, our approach significantly outperforms existing models, demonstrating its effectiveness and relevance in the complex domain of Big Data clustering.

HEXA-GAN: Skin lesion image inpainting via hexagonal sampling based generative adversarial network

Skin cancer is the uncontrollable proliferation of abnormal cells in the epidermis. In manual examination, the dermoscopic images are used by clinicians to detect skin cancer. However, digital hair removal (DHR) is more challenging task due to the presence of the hair occlusion that disturbs the intrinsic intensities and geometric features of the lesion regions that negatively affect diagnosis. Some of the dermis hairs in dermoscopic images are thicker, thinner, overlaps, fades, and obscures or covers textural changes. To overcome these challenges, a novel deep learning based Hexa-GAN model is proposed for hair segmentation and hair gap inpainting. Initially, the images from ISIC dataset are given to Attention U-net for segmenting the hairs in pixel units. The masked hair gap images from Attention U-net are given to the proposed Hexa-GAN with ReLU activation function. The first GAN (G1) performs coarse inpainting and reconstructs images using hexagonal sampling. Hexagonal divisions are merged into coarse inpainted images. The output of G1 is fed into the second GAN (G2) for fine reconstruction with contextual attention layer to generate the missing patches of the image. Finally, the output from G2 is merged into the original inpainted images without hair regions. From the experimental analysis, the Attention-Unet achieves better performance based on Jaccard index of 94.64% and Dice Index of 82.61% respectively. The proposed Hexa-GAN model increases the average PSNR range by 9.89%, 3.83%, 2.75%, 5.68%, 4.87% and 6.11% compared to CHC-Otsu model, Encoder-Decoder CNN, SN-PatchGAN, Variational Autoencoder, AlexNet architecture and FCN8-ResNetC approach respectively.

A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images

Background and objectives: This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network.Methods: The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information.Results: The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD).Conclusions: Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.

A combination of background modeler and encoder-decoder CNN for background/foreground segregation in image sequence

A combination of background modeler and encoder-decoder CNN for background/foreground segregation in image sequence

PLOSL: Population learning followed by one shot learning pulmonary image registration using tissue volume preserving and vesselness constraints.

This paper presents the Population Learning followed by One Shot Learning (PLOSL) pulmonary image registration method. PLOSL is a fast unsupervised learning-based framework for 3D-CT pulmonary image registration algorithm based on combining population learning (PL) and one-shot learning (OSL). The PLOSL image registration has the advantages of the PL and OSL approaches while reducing their respective drawbacks. The advantages of PLOSL include improved performance over PL, substantially reducing OSL training time and reducing the likelihood of OSL getting stuck in local minima. PLOSL pulmonary image registration uses tissue volume preserving and vesselness constraints for registration of inspiration-to-expiration and expiration-to-inspiration pulmonary CT images. A coarse-to-fine convolution encoder-decoder CNN architecture is used to register large and small shape features. During training, the sum of squared tissue volume difference (SSTVD) compensates for intensity differences between inspiration and expiration computed tomography (CT) images and the sum of squared vesselness measure difference (SSVMD) helps match the lung vessel tree. Results show that the PLOSL (SSTVD+SSVMD) algorithm achieved subvoxel landmark error while preserving pulmonary topology on the SPIROMICS data set, the public DIR-LAB COPDGene and 4DCT data sets.

Semantic Segmentation of Satellite Images using Deep Learning

Bidirectional in recent years, Deep learning performance in natural scene image processing has improved its use in remote sensing image analysis. In this paper, we used the semantic segmentation of remote sensing images for deep neural networks (DNN). To make it ideal for multi-target semantic segmentation of remote sensing image systems, we boost the Seg Net encoder-decoder CNN structures with index pooling & U-net. The findings reveal that the segmentation of various objects has its benefits and drawbacks for both models. Furthermore, we provide an integrated algorithm that incorporates two models. The test results indicate that the integrated algorithm proposed will take advantage of all multi-target segmentation models and obtain improved segmentation relative to two models. Keywords: A Satellite Image, Deep Neural Network, U-net, SigNet.

Multi-Task Head Pose Estimation in-the-Wild.

We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-decoder CNN with residual blocks and lateral skip connections. We show that the combination of head pose estimation and landmark-based face alignment significantly improve the performance of the former task. Further, the location of the pose task at the bottleneck layer, at the end of the encoder, and that of tasks depending on spatial information, such as visibility and alignment, in the final decoder layer, also contribute to increase the final performance. In the experiments conducted the proposed model outperforms the state-of-the-art in the face pose and visibility tasks. By including a final landmark regression step it also produces face alignment results on par with the state-of-the-art.

AUGMENTING DATA USING GAUSSIAN MIXTURE EMBEDDING FOR IMPROVING LAND COVER SEGMENTATION

Abstract. The use of convolutional neural networks improved greatly data synthesis in the last years and have been widely used for data augmentation in scenarios where very imbalanced data is observed, such as land cover segmentation. Balancing the proportion of classes for training segmentation models can be very challenging considering that samples where all classes are reasonably represented might constitute a small portion of a training set, and techniques for augmenting this small amount of data such as rotation, scaling and translation might be not sufficient for efficient training. In this context, this paper proposes a methodology to perform data augmentation from few samples to improve the performance of CNN-based land cover semantic segmentation. First, we estimate the latent data representation of selected training samples by means of a mixture of Gaussians, using an encoder-decoder CNN. Then, we change the latent embedding used to generate the mixture parameters, at random and in training time, to generate new mixture models slightly different from the original. Finally, we compute the displacement maps between the original and the modified mixture models, and use them to elastically deform the original images, creating new realistic samples out of the original ones. Our disentangled approach allows the spatial modification of displacement maps to preserve objects where deformation is undesired, like buildings and cars, where geometry is highly discriminant. With this simple pipeline, we managed to augment samples in training time, and improve the overall performance of two basal semantic segmentation CNN architectures for land cover semantic segmentation.

Encoder-decoder CNN models for automatic tracking of tongue contours in real-time ultrasound data.

One application of medical ultrasound imaging is to visualize and characterize human tongue shape and motion in real-time to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it requires knowledge about the tongue structure and ultrasound data interpretation for users to recognize tongue locations and gestures easily. Moreover, quantitative analysis of tongue motion needs the tongue contour to be extracted, tracked and visualized instead of the whole tongue region. Manual tongue contour extraction is a cumbersome, subjective, and error-prone task. Furthermore, it is not a feasible solution for real-time applications where the tongue contour moves rapidly with nuance gestures. This paper presents two new deep neural networks (named BowNet models) that benefit from the ability of global prediction of encoding-decoding fully convolutional neural networks and the capability of full-resolution extraction of dilated convolutions. Both qualitatively and quantitatively studies over datasets from two ultrasound machines disclosed the outstanding performances of the proposed deep learning models in terms of performance speed and robustness. Experimental results also revealed a significant improvement in the accuracy of prediction maps due to the better exploration and exploitation ability of the proposed network models.

Novel Volumetric Sub-region Segmentation in Brain Tumors.

A novel deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN) is proposed for effective, automated segmentation of different sub-regions viz. peritumoral edema (ED), necrotic core (NCR), enhancing and non-enhancing tumor core (ET/NET), from multi-modal MR images of the brain. An encoder-decoder type CNN model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal, and coronal) at the slice level. These are then combined, by incorporating a consensus fusion strategy with a fully connected Conditional Random Field (CRF) based post-refinement, to produce the final volumetric segmentation of the tumor and its constituent sub-regions. Concepts, such as spatial-pooling and unpooling are used to preserve the spatial locations of the edge pixels, for reducing segmentation error around the boundaries. A new aggregated loss function is also developed for effectively handling data imbalance. The MPS-CNN is trained and validated on the recent Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018 dataset. The Dice scores obtained for the validation set for whole tumor (WT :NCR/NE +ET +ED), tumor core (TC:NCR/NET +ET), and enhancing tumor (ET) are 0.90216, 0.87247, and 0.82445. The proposed MPS-CNN is found to perform the best (based on leaderboard scores) for ET and TC segmentation tasks, in terms of both the quantitative measures (viz. Dice and Hausdorff). In case of the WT segmentation it also achieved the second highest accuracy, with a score which was only 1% less than that of the best performing method.

A visual localization method based on encoder-decoder dual-stream CNN

A visual localization method based on encoder-decoder dual-stream CNN