Long-range Spatial Dependencies Research Articles

A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long-range spatial dependencies due to the locality of the convolutional layers. Transformers were introduced to address this challenge. In transformers with self-attention mechanism, even the first layer of information processing makes connections between distant image locations. Our paper presents a novel framework that bridges these two unique techniques, CNNs and transformers, to segment the gross tumor volume (GTV) accurately and efficiently in computed tomography (CT) images of non-small cell-lung cancer (NSCLC) patients. Under this framework, input of multiple resolution images was used with multi-depth backbones to retain the benefits of high-resolution and low-resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long-range dependency on the extracted features. To reduce computational complexity and to efficiently process multi-scale, multi-depth, high-resolution 3D images, this transformer pays attention to small key positions, which were identified by a self-attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models. The experimental results indicate that our proposed framework outperforms other CNN-based, transformer-based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto-segmentation of early-stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto-segmentation workflow is required. Our deep learning framework, based on CNN and transformer, performs auto-segmentation efficiently and could potentially assist clinical radiotherapy workflow.

Adaptive Locally-Aligned Transformer for low-light video enhancement

Low-light enhancement is a crucial task that aims to enhance the under-exposed input in computer vision. While state-of-the-art static single-image enhancement methods have made remarkable progress, yet, few attempts are explored the spatial-temporal sequence problem in low-light video enhancement. In this paper, we propose a simple yet highly effective method, termed as Adaptive Locally-Aligned Transformer (ALAT) for low-light video enhancement based on visual transformers. ALAT consists of three parts: feature encoder, locally-aligned transformer block (LATB) and pyramid feature decoder. Specifically, the transformer block enables the network to model the long-range spatial and appearance dependencies in videos due to its self-attention parallel computing mechanism. However, different from some previous approaches directly using the vanilla transformer, we consider that locality is significant in low-level vision tasks since the misaligned contextual local features (i.e., edges, shapes) may affect the prediction quality. Therefore, the proposed LATB is designed to align the video pixel with its most relevant ones adaptively in the local region to preserve the regional content information. Furthermore, we publish a new real-world low-light video dataset, named ExpressWay, to fill the gaps in the lack of dynamic low-light video scenarios, which contains high-quality videos with moving objects in both dark- and bright-light conditions. We conduct experiments on five benchmarks under three comprehensive settings including synthesized, static and our proposed dynamic low-light video datasets. Extensive experimental results show that our ALAT can outperform the previous state-of-the-arts by a large margin of 0.20∼1.10dB. Our method can be also extended to other video enhancement applications. The project is available at https://github.com/y1wencao/LLVE-ALAT.

Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network.

The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤ 4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms.

Image fusion for the novelty rotating synthetic aperture system based on vision transformer

Rotating synthetic aperture (RSA) technology offers a promising solution for achieving large-aperture and lightweight designs in optical remote-sensing systems. It employs a rectangular primary mirror, resulting in noncircular spatial symmetry in the point-spread function, which changes over time as the mirror rotates. Consequently, it is crucial to employ an appropriate image-fusion method to merge high-resolution information intermittently captured from different directions in the image sequence owing to the rotation of the mirror. However, existing image-fusion methods have struggled to address the unique imaging mechanism of this system and the characteristics of the geostationary orbit in which the system operates. To address this challenge, we model the imaging process of a noncircular rotating pupil and analyse its on-orbit imaging characteristics. Based on this analysis, we propose an image-fusion network based on a vision transformer. This network incorporates inter-frame mutual attention and intra-frame self-attention mechanisms, facilitating more effective extraction of temporal and spatial information from the image sequence. Specifically, mutual attention was used to model the correlation between pixels that were close to each other in the spatial and temporal dimensions, whereas long-range spatial dependencies were captured using intra-frame self-attention in the rotated variable-size attention block. We subsequently enhanced the fusion of spatiotemporal information using video swin transformer blocks. Extensive digital simulations and semi-physical imaging experiments on remote-sensing images obtained from the WorldView-3 satellite demonstrated that our method outperformed both image-fusion methods designed for the RSA system and state-of-the-art general deep learning-based methods.11Dataset is available at https://github.com/sherlockyusun/RSA-Fuse.

DeepEpi: Deep Learning Model for Predicting Gene Expression Regulation Based on Epigenetic Histone Modifications

Background: Histone modification is a vital element in gene expression regulation. The way in which these proteins bind to the DNA impacts whether or not a gene may be expressed. Although those factors cannot influence DNA construction, they can influence how it is transcribed. Objective: Each spatial location in DNA has its function, so the spatial arrangement of chromatin modifications affects how the gene can express. Also, gene regulation is affected by the type of histone modification combinations that are present on the gene and depends on the spatial distributional pattern of these modifications and how long these modifications read on a gene region. So, this study aims to know how to model Long-range spatial genome data and model complex dependencies among Histone reads. Methods: The Convolution Neural Network (CNN) is used to model all data features in this paper. It can detect patterns in histones signals and preserve the spatial information of these patterns. It also uses the concept of memory in long short-term memory (LSTM), using vanilla LSTM, Bi-Directional LSTM, or Stacked LSTM to preserve long-range histones signals. Additionally, it tries to combine these methods using ConvLSTM or uses them together with the aid of a self-attention. Results: Based on the results, the combination of CNN, LSTM with the self-attention mechanism obtained an Area under the Curve (AUC) score of 88.87% over 56 cell types. Conclusion: The result outperforms the present state-of-the-art model and provides insight into how combinatorial interactions between histone modification marks can control gene expression. The source code is available at https://github.com/RaniaHamdy/DeepEpi.

MD-SGT: Multi-dilation spherical graph transformer for unsupervised medical image registration.

Deformable medical image registration is an essential preprocess step for several clinical applications. Even though the existing convolutional neural network and transformer based methods achieved the promising results, the limited long-range spatial dependence and non-uniform attention span of these models prohibit further improving the registration performance. To deal with this issue, we proposed a multi-dilation spherical graph transformer (MD-SGT), in which the encoder combined the advantages of convolutional and graph transformer blocks to distinguish effectively the differences between the reference and the template images at various scales. Specifically, the features of each voxel were obtained by aggregating the information from its neighbors sampled from different spherical regions with different dilation rates. The implicit convolution inductive bias and long-range uniform attention span induced by such information aggregation manner made the features more representative for registration. Through the qualitative and quantitative comparisons with state-of-the-art methods on two datasets, we demonstrated that combining long-range uniform attention span and inductive bias are beneficial for promoting the image registration performance, with the Dice score, ASD and HD95 being improved at least by 0.5%, 2.2% and 1.1%, respectively.

A novel recurrent convolutional network based on grid correlation modeling for crowd flow prediction

Due to the increasing implementation of smart cities, crowd flow prediction has become a crucial aspect in various fields such as transportation management and public risk assessment. However, accurately predicting crowd flow can be influenced by various complex factors, including local and long-range spatial dependencies, as well as long-term temporal dependencies. Many works have attempted to model long-range spatial dependence relying on stacked convolutional layers, failing to directly consider it from a global perspective. In this article, we propose a novel recurrent convolutional network based on grid correlation, named RCNGC. In this model, the correlation between different grids based on crowd flow pattern is identified first. Then, a deep network following an encoding-prediction framework is proposed. Specifically, a novel GC-ConvLSTM module based on the grid correlation is utilized to capture long-range dependence in both temporal and spatial dimensions in encoding phase while ConvLSTM is leveraged in prediction phase. We conduct extensive experiments on a dataset in Futian, China. The results demonstrate that the proposed RCNGC is superior to baseline methods in multi-step prediction, indicating that identifying the correlation between grids preliminarily can improve the precision of prediction.

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems. To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Specifically, we design a spatial self-attention module to capture the dynamic spatial dependencies. Then, two graph masking matrices are introduced to highlight spatial dependencies from short- and long-range views. Moreover, a traffic delay-aware feature transformation module is proposed to empower PDFormer with the capability of explicitly modeling the time delay of spatial information propagation. Extensive experimental results on six real-world public traffic datasets show that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Moreover, we visualize the learned spatial-temporal attention map to make our model highly interpretable.

Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition

Graph convolutional networks (GCN) have attracted increasing interest in action recognition in recent years. GCN models human skeleton sequences as spatio-temporal graphs. Also, attention mechanisms are often jointly used with GCNs to highlight important frames or body joints in a sequence. However, attention modules learn parameters offline and are fixed, so may not adapt well to unseen samples. In this paper, we propose a simple but effective motion-driven spatial and temporal adaptation strategy to dynamically strengthen the features of important frames and joints for skeleton-based action recognition. The rationale is that the joints and frames with dramatic motions are generally more informative and discriminative. We combine the spatial and temporal refinements by using a two-branch structure, in which the joint and frame-wise feature refinements perform in parallel. Such a structure can lead to learn more complementary feature representations. Moreover, we propose to use the fully connected graph convolution to learn the long-range spatial dependencies. Besides, we investigate two high-resolution skeleton graphs by creating virtual joints, aiming to improve the representation of skeleton features. By combining the above proposals, we develop a novel motion-driven spatial and temporal adaptive high-resolution GCN. Experimental results demonstrate that the proposed model achieves state-of-the-art (SOTA) results on the challenging large-scale Kinetics-Skeleton and UAV-Human datasets, and it is on par with the SOTA methods on the two NTU-RGB+D 60&120 datasets. Additionally, our motion-driven adaptation method shows encouraging performance when compared with the attention mechanisms.

MS-CANet: Multi-Scale Subtraction Network with Coordinate Attention for Retinal Vessel Segmentation

Retinal vessel segmentation is crucial in the diagnosis of certain ophthalmic and cardiovascular diseases. Although U-shaped networks have been widely used for retinal vessel segmentation, most of the improved methods have insufficient feature extraction capability and fuse different network layers using element or dimension summation, leading to redundant information and inaccurate retinal vessel localization with blurred vessel edges. The asymmetry of small blood vessels in fundus images also increases the difficulty of segmenting blood vessels. To overcome these challenges, we propose a novel multi-scale subtraction network (MS-CANet) with residual coordinate attention to segment the vessels in retinal vessel images. Our approach incorporates a residual coordinate attention module during the encoding phase, which captures long-range spatial dependencies while preserving precise position information. To obtain rich multi-scale information, we also include multi-scale subtraction units at different perceptual field levels. Moreover, we introduce a parallel channel attention module that enhances the contrast between vessel and background, thereby improving the detection of marginal vessels during the decoding phase. We validate our proposed model on three benchmark datasets, namely DRIVE, CHASE, and STARE. The results demonstrate that our method outperforms most advanced methods under different evaluation metrics.

Dual-aware transformer network for single-image super-resolution

The advent of deep learning has enabled image super-resolution (SR) technology to achieve tremendous success. The recent exploration between convolutional neural network (CNN) and transformer has produced an impressive performance. However, the huge model size and heavy computational cost occupied by most approaches cannot be ignored. Additionally, the global and local modeling of image features is not well utilized and explored, which is important for texture content reconstruction. In this study, we present an efficient and lightweight network based on CNN and transformer for image SR, dubbed dual-aware transformer network (DATN). Specifically, a dual-aware fusion module (DAFM), consisting of spatial-aware adaptive block (SAAB) and global-aware fusion block (GAFB), learns local and global features in a complementary manner for yielding abundant content details. SAAB delivers the captured spatial information to GAFB in parallel, whereas GAFB establishes long-range spatial dependencies among diverse features to boost the network’s ability for recovering texture details. Further, in the upsampling stage, our proposed transformer-empowered upsampling module aggregates global information from the transformer module to generate location-wise reassembly kernels for content-aware upsampling. In such way, feature-rich information is highlighted adaptively to reconstruct high-quality images with maximum efficiency. Experimental results reveal that our proposed DATN achieves high qualitative performance while maintaining low computational demands, surpassing most state-of-the-art SR networks.

RNLFNet: Residual non-local Fourier network for undersampled MRI reconstruction

Magnetic Resonance Imaging (MRI) has been widely applied in medical clinical diagnosis. Generally, obtaining a high spatial resolution MR image takes up to tens of minutes long. Reconstructing MR images from the undersampled k-space data has been playing a crucial role to accelerate MRI. Especially, the deep Convolutional Neural Networks (CNNs) have shown potential to significantly accelerate MRI. However, the receptive field size of CNNs is relatively small and it fails to capture the long-range dependencies. Nowadays, the non-local attention has been successfully applied in vision tasks due to the advantages in capturing long-range dependencies. However, the existing non-local attention generally learns long-range interactions among spatial locations in the spatial domain. It rarely involves in the frequency domain, which are likely to lead to beneficial outcomes. Recently, there are investigations that start to combine Fourier Transform with deep neural networks. In this work, we consider to learn the long-range interactions from the perspective of frequency. Specifically, we design a novel Non-Local Fourier Attention (NLFA) that combines the self-attention mechanism with Fourier Transform to capture the long-range spatial dependencies in the frequency domain. Furthermore, a new deep Residual Non-Local Fourier Network (RNLFNet) constructed with the Non-Local Fourier Attention and Residual Blocks is proposed for accelerated MRI. Such framework focuses on learning the information from both the spatial and frequency domain, which enjoys benefits from modelling both local details and global context between the degraded MR image and ground truth image pairs. The proposed model is evaluated on the MICCAI grand challenge datasets and fastMRI datasets, which significantly boosts the MR image reconstruction performance.

Boosting UAV Tracking With Voxel-Based Trajectory-Aware Pre-Training

Siamese network-based object tracking has remarkably promoted the automatic capability for highly-maneuvered unmanned aerial vehicles (UAVs). However, the leading-edge tracking framework often depends on template matching, making it trapped when facing multiple views of object in consecutive frames. Moreover, the general image-level pretrained backbone can overfit to holistic representations, causing the misalignment to learn object-level properties in UAV tracking. To tackle these issues, this work presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRTrack</i> , a comprehensive framework to fully exploit the stereoscopic representation for UAV tracking. Specifically, a novel pre-training paradigm method is proposed. Through trajectory-aware reconstruction training (TRT), the capability of the backbone to extract stereoscopic structure feature is strengthened without any parameter increment. Accordingly, an innovative hierarchical self-attention Transformer is proposed to capture the local detail information and global structure knowledge. For optimizing the correlation map, we proposed a novel spatial correlation refinement (SCR) module, which promotes the capability of modeling the long-range spatial dependencies. Comprehensive experiments on three UAV challenging benchmarks demonstrate that the proposed TRTrack achieves superior UAV tracking performance in both precision and efficiency. Quantitative tests in real-world settings fully prove the effectiveness of our work.

Attention Mechanism Trained with Small Datasets for Biomedical Image Segmentation

The understanding of long-range pixel–pixel dependencies plays a vital role in image segmentation. The use of a CNN plus an attention mechanism still has room for improvement, since existing transformer-based architectures require many thousands of annotated training samples to model long-range spatial dependencies. This paper presents a smooth attention branch (SAB), a novel architecture that simplifies the understanding of long-range pixel–pixel dependencies for biomedical image segmentation in small datasets. The SAB is essentially a modified attention operation that implements a subnetwork via reshaped feature maps instead of directly calculating a softmax value over the attention score for each input. The SAB fuses multilayer attentive feature maps to learn visual attention in multilevel features. We also introduce position blurring and inner cropping specifically for small-scale datasets to prevent overfitting. Furthermore, we redesign the skip pathway for the reduction of the semantic gap between every captured feature of the contracting and expansive path. We evaluate the architecture of U-Net with the SAB (SAB-Net) by comparing it with the original U-Net and widely used transformer-based models across multiple biomedical image segmentation tasks related to the Brain MRI, Heart MRI, Liver CT, Spleen CT, and Colonoscopy datasets. Our training set was made of random 100 images of the original training set, since our goal was to adopt attention mechanisms for biomedical image segmentation tasks with small-scale labeled data. An ablation study conducted on the brain MRI test set demonstrated that every proposed method achieved an improvement in biomedical image segmentation. Integrating the proposed methods helped the resulting models consistently achieve outstanding performance on the above five biomedical segmentation tasks. In particular, the proposed method with U-Net improved its segmentation performance over that of the original U-Net by 13.76% on the Brain MRI dataset. We proposed several novel methods to address the need for modeling long-range pixel–pixel dependencies in small-scale biomedical image segmentation. The experimental results illustrated that each method could improve the medical image segmentation accuracy to various degrees. Moreover, SAB-Net, which integrated all proposed methods, consistently achieved outstanding performance on the five biomedical segmentation tasks.

Transformer-Based Self-Supervised Monocular Depth and Visual Odometry

Self-supervised monocular depth and visual odometry (VO) are often cast as coupled tasks. Accurate depth contributes to precise pose estimation and vice versa. Existing architectures typically exploit stacking convolution layers and long short-term memory (LSTM) units to capture long-range dependencies. However, their intrinsic locality hinders the model from getting the expected performance gain. In this article, we propose a Transformer-based architecture, named Transformer-based self-supervised monocular depth and VO (TSSM-VO), to tackle these problems. It comprises two main components: 1) a depth generator that leverages the powerful capability of multihead self-attention (MHSA) on modeling long-range spatial dependencies and 2) a pose estimator built upon a Transformer to learn long-range temporal correlations of image sequences. Moreover, a new data augmentation loss based on structural similarity (SSIM) is introduced to constrain further the structural similarity between the augmented depth and the augmented predicted depth. Rigorous ablation studies and exhaustive performance comparison on the KITTI and Make3D datasets demonstrate the superiority of TSSM-VO over other self-supervised methods. We expect that TSSM-VO would enhance the ability of intelligent agents to understand the surrounding environments.

SAR and Multi-Spectral Data Fusion for Local Climate Zone Classification with Multi-Branch Convolutional Neural Network

The local climate zone (LCZ) scheme is of great value for urban heat island (UHI) effect studies by providing a standard classification framework to describe the local physical structure at a global scale. In recent years, with the rapid development of satellite imaging techniques, both multi-spectral (MS) and synthetic aperture radar (SAR) data have been widely used in LCZ classification tasks. However, the fusion of MS and SAR data still faces the challenges of the different imaging mechanisms and the feature heterogeneity. In this study, to fully exploit and utilize the features of SAR and MS data, a data-grouping method was firstly proposed to divide multi-source data into several band groups according to the spectral characteristics of different bands. Then, a novel network architecture, namely Multi-source data Fusion Network for Local Climate Zone (MsF-LCZ-Net), was introduced to achieve high-precision LCZ classification, which contains a multi-branch CNN for multi-modal feature extraction and fusion, followed by a classifier for LCZ prediction. In the proposed multi-branch structure, a split–fusion-aggregate strategy was adopted to capture multi-level information and enhance the feature representation. In addition, a self channel attention (SCA) block was introduced to establish long-range spatial and inter-channel dependencies, which made the network pay more attention to informative features. Experiments were conducted on the So2Sat LCZ42 dataset, and the results show the superiority of our proposed method when compared with state-of-the-art methods. Moreover, the LCZ maps of three main cities in China were generated and analyzed to demonstrate the effectiveness of our proposed method.

Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling

Although most models for rainfall extremes focus on pointwise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To capture the joint behaviour of precipitation aggregates evaluated at different spatial scales, parsimonious and effective models must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at different scales and intensities, e.g., convective and frontal, with extremes of aggregates for typical catchment sizes arising from extremes of only one of these processes, rather than a combination of them. High-intensity convective events cause extreme spatial aggregates at small scales but the contribution of lower-intensity large-scale fronts is likely to increase as the area aggregated increases. Thus, to capture small to large scale spatial aggregates within a single approach requires a model that can accurately capture the extremal properties of both convective and frontal events. Previous extreme value methods have ignored this mixture structure; we propose a spatial extreme value model which is a mixture of two components with different marginal and dependence models that are able to capture the extremal behaviour of convective and frontal rainfall and more faithfully reproduces spatial aggregates for a wide range of scales. Modelling extremes of the frontal component raises new challenges due to it exhibiting strong long-range extremal spatial dependence. Our modelling approach is applied to fine-scale, high-dimensional, gridded precipitation data. We show that accounting for the mixture structure improves the joint inference on extremes of spatial aggregates over regions of different sizes.

RockFormer: A U-Shaped Transformer Network for Martian Rock Segmentation

Martian rock segmentation aims to separate rock pixels from background, which plays a crucial role in downstream tasks, such as traversing and geologic analysis by Mars rovers. The U-Nets have achieved certain results in rock segmentation. However, due to the inherent locality of convolution operations, U-Nets are inadequate in modeling global context and long-range spatial dependencies. Although emerging Transformers can solve this, they suffer from difficulties in extracting and retaining sufficient low-level local information. These shortcomings limit the performance of the existing networks for Martian rocks that are variable in shape, size, texture, and color. Therefore, we propose RockFormer, the first U-shaped Transformer framework for Mars rock segmentation, consisting of a hierarchical encoder–decoder architecture with a feature refining module (FRM) connected between them. Specifically, the encoder hierarchically generates multiscale features using an improved vision Transformer (improved-ViT), where both abundant local information and long-range contexts are exploited. The FRM removes less representative features and captures global dependencies between multiscale features, improving RockFormer’s robustness to Martian rocks with diverse appearances. The decoder is responsible for aggregating these features for pixelwise rock prediction. For evaluation, we establish two Mars rock datasets, including both real and synthesized images. One is MarsData-V2, an extension of our previously published MarsData collected from real Mars rocks. The other is SynMars, a synthetic dataset sequentially photographed from a virtual terrain built referring to the TianWen-1 dataset. Extensive experiments on the two datasets show the superiority of RockFormer for Martian rock segmentation, achieving state-of-the-art performance with decent computational simplicity.

QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration.

Synthesis of computed tomography (CT) images from magnetic resonance (MR) images is an important task to overcome the lack of electron density information in MR-only radiotherapy treatment planning (RTP). Some innovative methods have been proposed for abdominal MR-to-CT synthesis. However, it is still challenging due to the large misalignment between preprocessed abdominal MR and CT images and the insufficient feature information learned by models. Although several studies have used the MR-to-CT synthesis to alleviate the difficulty of multi-modal registration, this misalignment remains unsolved when training the MR-to-CT synthesis model. In this paper, we propose an end-to-end quartet attention aware closed-loop learning (QACL) framework for MR-to-CT synthesis via simultaneous registration. Specifically, the proposed quartet attention generator and mono-modal registration network form a closed-loop to improve the performance of MR-to-CT synthesis via simultaneous registration. In particular, a quartet-attention mechanism is developed to enlarge the receptive fields in networks to extract the long-range and cross-dimension spatial dependencies. Experimental results on two independent abdominal datasets demonstrate that our QACL achieves impressive results with MAE of 55.30±10.59 HU, PSNR of 22.85±1.43 dB, and SSIM of 0.83±0.04 for synthesis, and with Dice of 0.799±0.129 for registration. The proposed QACL outperforms the state-of-the-art MR-to-CT synthesis and multi-modal registration methods.

Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme

Intelligent lung nodules classification is a meaningful and challenging research topic for early precaution of lung cancers, which aims to diagnose the malignancy of candidate nodules from the pulmonary computed tomography images. Nowadays, deep learning methods have made significant achievements in the medical field and promoted developments of lung nodules classification. Nevertheless, mainstream CNNs-based networks typically excel in learning coarse-grained local feature representations via stacked local-aware and weight-shared convolutions, and cannot practically model the long-range context interaction and the spatial dependencies. To tackle the above difficulties, we innovatively propose an effective Multi-Granularity Dilated Transformer to learn the long-range context relations, and explore fine-grained local details via the proposed Local Focus Scheme. Specifically, we delicately design a novel Deformable Dilated Transformer to incorporate diverse contextual information with self-attention for learning long-range global spatial dependencies. Moreover, numerous investigations indicate that local details are extremely crucial to classify indistinguishable lung nodules. Thus, we propose the Local Focus Scheme to focus on the more discriminative local features by modeling channel-wise grouped topology. Consequently, the Multi-Granularity Dilated Transformer is constructed by leveraging the Local Focus Scheme to guide the Deformable Dilated Transformer for learning fine-grained local cues. Experimental results on the mainstream benchmark LIDC-IDRI demonstrate the superiority of our model compared with the state-of-the-art methods.