Residual Encoder Research Articles

Asym-UNet: An asymmetric U-shape Network for breast lesions ultrasound images segmentation

Precise segmentation of breast ultrasound images is essential for early breast cancer screening. However, the segmentation process is challenging due to the diverse morphology of tumors, blurred boundaries, and the similar grey intensity distribution between lesions and normal tissues. To overcome these obstacles, we propose the Asym-UNet, an asymmetric U-shaped network tailored for segmenting breast lesions with high accuracy and reliability. This network features a Multi-branch Residual Encoder (MRE), an External Attention Module (EAM), and a Boundary Detection Module (BDM). The MRE is designed to capture distinctive representations of breast lesions, facilitating accurate subsequent segmentation. The EAM directs the network to focus on the lesion areas while filtering out irrelevant information, thereby enhancing diagnostic precision. Furthermore, the BDM, incorporated to refine the boundaries of predicted masks, provides additional supervision and guidance through a multi-level prediction layer. We conducted evaluations of the Asym-UNet against thirteen other segmentation methods using two publicly available datasets. The results indicate that our network achieves state-of-the-art performance in breast tumor segmentation.

Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework

Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure.

Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment.

The volume measurement of intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) provides critical information for precise treatment of patients with spontaneous ICH but remains a big challenge, especially for IVH segmentation. However, the previously proposed ICH and IVH segmentation tools lack external validation and segmentation quality assessment. This study aimed to develop a robust deep learning model for the segmentation of ICH and IVH with external validation, and to provide quality assessment for IVH segmentation. In this study, a Residual Encoding Unet (REUnet) for the segmentation of ICH and IVH was developed using a dataset composed of 977 CT images (all contained ICH, and 338 contained IVH; a five-fold cross-validation procedure was adopted for training and internal validation), and externally tested using an independent dataset consisting of 375 CT images (all contained ICH, and 105 contained IVH). The performance of REUnet was compared with six other advanced deep learning models. Subsequently, three approaches, including Prototype Segmentation (ProtoSeg), Test Time Dropout (TTD), and Test Time Augmentation (TTA), were employed to derive segmentation quality scores in the absence of ground truth to provide a way to assess the segmentation quality in real practice. For ICH segmentation, the median (lower-quantile-upper quantile) of Dice scores obtained from REUnet were 0.932 (0.898-0.953) for internal validation and 0.888 (0.859-0.916) for external test, both of which were better than those of other models while comparable to that of nnUnet3D in external test. For IVH segmentation, the Dice scores obtained from REUnet were 0.826 (0.757-0.868) for internal validation and 0.777 (0.693-0.827) for external tests, which were better than those of all other models. The concordance correlation coefficients between the volumes estimated from the REUnet-generated segmentations and those from the manual segmentations for both ICH and IVH ranged from 0.944 to 0.987. For IVH segmentation quality assessment, the segmentation quality score derived from ProtoSeg was correlated with the Dice Score (Spearman r=0.752 for the external test) and performed better than those from TTD (Spearman r=0.718) and TTA (Spearman r=0.260) in the external test. By setting a threshold to the segmentation quality score, we were able to identify low-quality IVH segmentation results by ProtoSeg. The proposed REUnet offers a promising tool for accurate and automated segmentation of ICH and IVH, and for effective IVH segmentation quality assessment, and thus exhibits the potential to facilitate therapeutic decision-making for patients with spontaneous ICH in clinical practice.

Enhancing the expressivity of quantum neural networks with residual connections

In noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to O(l2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({l}^{2})$$\\end{document}, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.

DON6D: a decoupled one-stage network for 6D pose estimation

The six-dimensional (6D) pose object estimation is a key task in robotic manipulation and grasping scenes. Many existing two-stage solutions with a slow inference speed require extra refinement to handle the challenges of variations in lighting, sensor noise, object occlusion, and truncation. To address these challenges, this work proposes a decoupled one-stage network (DON6D) model for 6D pose estimation that improves inference speed on the premise of maintaining accuracy. Particularly, since the RGB images are aligned with the RGB-D images, the proposed DON6D first uses a two-dimensional detection network to locate the interested objects in RGB-D images. Then, a module of feature extraction and fusion is used to extract color and geometric features fully. Further, dual data augmentation is performed to enhance the generalization ability of the proposed model. Finally, the features are fused, and an attention residual encoder–decoder, which can improve the pose estimation performance to obtain an accurate 6D pose, is introduced. The proposed DON6D model is evaluated on the LINEMOD and YCB-Video datasets. The results demonstrate that the proposed DON6D is superior to several state-of-the-art methods regarding the ADD(-S) and ADD(-S) AUC metrics.

Self-equilibrium segmentation of near-infrared images of dental microcracks

The issue of missing teeth caused by dental microcracks has become a central topic in dental clinical medicine. Near-infrared technology combined with advanced image segmentation algorithms has become one of the leading technical routes for dental clinical medicine diagnosis. In this study, the NIR image segmentation method of dental microcracks was studied in depth to further utilize the advantages of near-infrared imaging technology in the detection technology of dental microcracks. The main contributions and research contents of this paper include (1) we designed a near-infrared imaging system for dental imaging, which provides solid data support for the study of advanced segmentation algorithms; (2) we designed a self-equilibrium segmentation algorithm for occult fracture of teeth based on U-Net++. The core module of this algorithm includes a multi-resolution residual encoder and a self-equilibrium composite loss function. The multi-scale residual encoder significantly improves the feature extraction capability of U-Net++. The self-equilibrium combined loss function ensures the consistency of the model’s attention to positive and negative samples, which enhances the robustness of U-Net++. (3) We have demonstrated through extensive experiments that our algorithm performs better than other dental occultation segmentation methods. This study is valuable for improving the diagnostic accuracy of dental occult fractures and promptly detecting the potential danger of the dental.

Exploring the Application of Large-Scale Pre-Trained Models on Adverse Weather Removal.

Image restoration under adverse weather conditions (e.g., rain, snow, and haze) is a fundamental computer vision problem that has important implications for various downstream applications. Distinct from early methods that are specially designed for specific types of weather, recent works tend to simultaneously remove various adverse weather effects based on either spatial feature representation learning or semantic information embedding. Inspired by various successful applications incorporating large-scale pre-trained models (e.g., CLIP), in this paper, we explore their potential benefits for leveraging large-scale pre-trained models in this task based on both spatial feature representation learning and semantic information embedding aspects: 1) spatial feature representation learning, we design a Spatially Adaptive Residual (SAR) encoder to adaptively extract degraded areas. To facilitate training of this model, we propose a Soft Residual Distillation (CLIP-SRD) strategy to transfer spatial knowledge from CLIP between clean and adverse weather images; 2) semantic information embedding, we propose a CLIP Weather Prior (CWP) embedding module to enable the network to adaptively respond to different weather conditions. This module integrates the sample-specific weather priors extracted by the CLIP image encoder with the distribution-specific information (as learned by a set of parameters) and embeds these elements using a cross-attention mechanism. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art performance under various and severe adverse weather conditions. The code will be made available.

Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding.

To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications. The proposed method consists of three main components: (1) an unsupervised network that combines Residual Encoder and Restricted Subspace Mapping (RERSM-net) and is trained to generate a phase-difference map based on the even and odd SPEN images; (2) a spin physical forward model to obtain the corrected image with the learned phase difference map; and (3) cycle-consistency loss that is explored for training the RERSM-net. The proposed RERSM-net could effectively generate smooth phase difference maps and correct Nyquist ghosts of single-shot SPEN. Both simulation and real in vivo MRI experiments demonstrated that our method outperforms the state-of-the-art SPEN Nyquist ghost correction method. Furthermore, the ablation experiments of generating phase-difference maps show the advantages of the proposed unsupervised model. The proposed method can effectively correct Nyquist ghosts for the single-shot SPEN sequence.

RTAU-Net: A novel 3D rectal tumor segmentation model based on dual path fusion and attentional guidance

Background and objectiveAccording to the Global Cancer Statistics 2020, colorectal cancer has the third-highest diagnosis rate (10.0 %) and the second-highest mortality rate (9.4 %) among the 36 types. Rectal cancer accounts for a large proportion of colorectal cancer. The size and shape of the rectal tumor can directly affect the diagnosis and treatment by doctors. The existing rectal tumor segmentation methods are based on two-dimensional slices, which cannot analyze a patient's tumor as a whole and lose the correlation between slices of MRI image, so the practical application value is not high. MethodsIn this paper, a three-dimensional rectal tumor segmentation model is proposed. Firstly, image preprocessing is performed to reduce the effect caused by the unbalanced proportion of background region and target region, and improve the quality of the image. Secondly, a dual-path fusion network is designed to extract both global features and local detail features of rectal tumors. The network includes two encoders, a residual encoder for enhancing the spatial detail information and feature representation of the tumor and a transformer encoder for extracting global contour information of the tumor. In the decoding stage, we merge the information extracted from the dual paths and decode them. In addition, for the problem of the complex morphology and different sizes of rectal tumors, a multi-scale fusion channel attention mechanism is designed, which can capture important contextual information of different scales. Finally, visualize the 3D rectal tumor segmentation results. ResultsThe RTAU-Net is evaluated on the data set provided by Shanxi Provincial Cancer Hospital and Xinhua Hospital. The experimental results showed that the Dice of tumor segmentation reached 0.7978 and 0.6792, respectively, which improved by 2.78 % and 7.02 % compared with suboptimal model. ConclusionsAlthough the morphology of rectal tumors varies, RTAU-Net can precisely localize rectal tumors and learn the contour and details of tumors, which can relieve physicians' workload and improve diagnostic accuracy.

Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images.

Automated retinal blood vessel segmentation in fundus images provides important evidence to ophthalmologists in coping with prevalent ocular diseases in an efficient and non-invasive way. However, segmenting blood vessels in fundus images is a challenging task, due to the high variety in scale and appearance of blood vessels and the high similarity in visual features between the lesions and retinal vascular. Inspired by the way that the visual cortex adaptively responds to the type of stimulus, we propose a Stimulus-Guided Adaptive Transformer Network (SGAT-Net) for accurate retinal blood vessel segmentation. It entails a Stimulus-Guided Adaptive Module (SGA-Module) that can extract local-global compound features based on inductive bias and self-attention mechanism. Alongside a light-weight residual encoder (ResEncoder) structure capturing the relevant details of appearance, a Stimulus-Guided Adaptive Pooling Transformer (SGAP-Former) is introduced to reweight the maximum and average pooling to enrich the contextual embedding representation while suppressing the redundant information. Moreover, a Stimulus-Guided Adaptive Feature Fusion (SGAFF) module is designed to adaptively emphasize the local details and global context and fuse them in the latent space to adjust the receptive field (RF) based on the task. The evaluation is implemented on the largest fundus image dataset (FIVES) and three popular retinal image datasets (DRIVE, STARE, CHASEDB1). Experimental results show that the proposed method achieves a competitive performance over the other existing method, with a clear advantage in avoiding errors that commonly happen in areas with highly similar visual features. The sourcecode is publicly available at: https://github.com/Gins-07/SGAT.

Adaptive multi-scale TF-net for high-resolution time–frequency representations

A novel adaptive multi-scale time–frequency network (AMTFN) is proposed to provide high-resolution time–frequency representations for nonstationary signals. AMTFN is an end-to-end deep network, which firstly adaptively learns the comprehensive basis functions to produce time–frequency (TF) feature maps through multi-scale 1D convolutional kernels. Then, the channel attention mechanism is embedded into AMTFN to rescale the TF feature maps selectively. Thus, the subsequent residual encoder–decoder block’s energy concentration performance is greatly improved with these rescaled TF feature maps. Besides, this paper designs a new training strategy to elegantly enable the model to pay more attention to the intersections of instantaneous frequency trajectories. In the end, a series of simulations as well as real-world cases, are studied to demonstrate the effectiveness and advantages of the proposed method.

Hierarchical Residual Encoding for Multiresolution Time Series Compression

Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation. One challenge in lossy compression is that different applications may have different accuracy demands. Today's compression techniques struggle in this setting either forcing the user to compress at the strictest accuracy demand, or to re-encode the data at multiple resolutions. This paper proposes a simple, but effective multiresolution compression algorithm for time series data, where a single encoding can effectively be decompressed at multiple output resolutions. There are a number of benefits over current state-of-the-art techniques for time series compression. (1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression latency is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency of our encoding is significantly faster than single encodings at the strictest accuracy demand.

Multiscale features integration based multiple-in-single-out network for object detection

The single-level feature map-based object detection has been a challenging task due to the feature scale limitation. Therefore, enriching multiscale information of single-level features is considered a promising approach to deal with this challenge. Although most existing methods have attempted to augment the feature scale of single-level features, the detection performance is still unsatisfactory because these methods mine multiscale features only based on a one-level feature map. To address this problem, we propose a multiple-in-single-out network (MiSoNet) to integrate multiscale information from multilevel feature maps into a single-level feature map. To achieve this, MiSoNet’s key component is equipped with two cascaded modules: a multilevel feature integration module (MFIM) and a depthwise convolutional residual encoder (DWEncoder). Specifically, MFIM adaptively fuses features of inconsistent semantics and scales from multilevel feature maps. DWEncoder stacks several residual blocks with depthwise convolutions to extract multiscale contexts in the single feature map, which can further extend the scale range of the receptive fields. Extensive experiments are conducted on the Common Objects in Context (COCO) dataset, where the MiSoNet achieves a 41.0AP, which surpasses the YOLOF by 1.4AP with negligible computational overhead. Moreover, the MiSoNet, with fewer parameters and FLOPs, outperforms some advanced detectors based on the feature pyramid network.

Aligning Image Semantics and Label Concepts for Image Multi-Label Classification

Image multi-label classification task is mainly to correctly predict multiple object categories in the images. To capture the correlation between labels, graph convolution network based methods have to manually count the label co-occurrence probability from training data to construct a pre-defined graph as the input of graph network, which is inflexible and may degrade model generalizability. Moreover, most of the current methods cannot effectively align the learned salient object features with the label concepts, so that the predicted results of model may not be consistent with the image content. Therefore, how to learn the salient semantic features of images and capture the correlation between labels, and then effectively align them is one of the key to improve the performance of image multi-label classification task. To this end, we propose a novel image multi-label classification framework which aims to align I mage S emantics with L abel C oncepts ( ISLC ). Specifically, we propose a residual encoder to learn salient object features in the images, and exploit the self-attention layer in aligned decoder to automatically capture the correlation between labels. Then, we leverage the cross-attention layers in aligned decoder to align image semantic features with label concepts, so as to make the labels predicted by model more consistent with image content. Finally, the output features of the last layer of residual encoder and aligned decoder are fused to obtain the final output feature for classification. The proposed ISLC model achieves good performance on various prevalent multi-label image datasets such as MS-COCO 2014, PASCAL VOC 2007, VG-500, and NUS-WIDE with 87.2%, 96.9%, 39.4%, and 64.2%, respectively.

Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques.

Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2-18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost. Thus, methods that can transform strongly bandlimited ultrasound data into broadband data are essential. In this work, we propose a deep learning (DL) technique to improve the image quality for a given bandwidth by learning features provided by broadband data of the same field of view. Therefore, the performance of several DL architectures and conventional state-of-the-art techniques for image quality improvement and artifact removal have been compared on in vitro US datasets. Two training losses have been utilized on three different architectures: a super resolution convolutional neural network (SRCNN), U-Net, and a residual encoder decoder network (REDNet) architecture. The models have been trained to transform low-bandwidth image reconstructions to high-bandwidth image reconstructions, to reduce the artifacts, and make the reconstructions visually more attractive. Experiments were performed for 20%, 40%, and 60% fractional bandwidth on the original images and showed that the improvements obtained are as high as 45.5% in RMSE, and 3.85 dB in PSNR, in datasets with a 20% bandwidth limitation.

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this studyis to look at people's perceptions of the COVID-19pandemicwho interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020,during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.

EEG-dependent automatic speech recognition using deep residual encoder based VGG net CNN

Speech difficulties are common in children and teenagers, but they can also occur in adults as a result of physical problems. A speech disorder is a situation in which an individual struggles to produce or construct the spoken sounds necessary for interpersonal communication. As a result, it could be challenging to comprehend the person's speech. Articulation abnormalities are typical speech problems. In this situation, automatic speech recognition (ASR) technology may be used to detect and further rectify such deficiencies. The first attempts to detect speech abnormalities were made in the early 1970s, and they appear to have followed the same path as those on the ASR. These early experiments did rely heavily on signal processing techniques. As time goes on, more ideas from ASR technology are being incorporated into systems that deal with speech impairments. Many traditional techniques are executed in the ASR system. In this paper, we developed an automatic speech recognition technology based on deep learning techniques. In this paper, we research alternative extraction and classification methods of electroencephalography (EEG) to help diagnose speech disorders (SD). The EEG data is prepared before degradation into numerous EEG sub-strands with a discrete wavelet transformation to eliminate unimportant errors. For sharpening signals, the Eigenvector crack curvature wavelet method was used. A hyper-similarity abnormality coder is used for feature extraction in the EEG recording and to detect synchronization between EEG channels, which may show abnormalities in communication. The recovered functions are then categorized using the Deep Residual–encoder–based VGG net CNN Classification Method. Thus, the techniques proposed to produce the most promising outcome aren't the suggested technique attained better classification accuracy when compared to the traditional methodologies.

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.

To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)-GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model's encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients' survival days.

Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

To suppress noise and artifacts caused by the reduced radiation exposure in low-dose computed tomography, several deep learning (DL)-based image restoration methods have been proposed over the past few years. Many of these popular DL-based methods adopt an encoder–decoder framework, for instance, the residual encoder–decoder convolutional neural network. However, this popular framework may suffer from information loss for continual downsampling operations. In this article, deep cascaded residual networks (DCRNs) are proposed to optimize the popular encoder–decoder network. First, cross up- and downsampling operations as well as attention extraction are substitutes for the strict “downsampling and then upping” principle. What is more, four hybrid loss functions, namely, mean absolute error, edge loss, perceptual loss and adversarial loss, are engaged to achieve better visual effects and suppress noise. The experiments are conducted on three individual clinical CT datasets: dental CT data collected with a scanner manufactured by Zhongke Tianyue Company (ZTC), data from the American Association of Physicists in Medicine (AAPM) Challenge, and data collected with a commercial CT scanner from United Imaging Healthcare (UIH). The experimental results indicate the effective noise reduction and detail preservation capabilities of the proposed methods under different radiation dose-reduction strategies.

Speckle denoising of optical coherence tomography image using residual encoder–decoder CycleGAN

Speckle denoising of optical coherence tomography image using residual encoder–decoder CycleGAN