Gravity inversion aims to recover subsurface density distributions from surface anomalies, but the inverse mapping is severely ill-posed, non-unique, and highly sensitive to burial depth, target scale, and observation noise. To enhance long range anomaly-source modeling and boundary recovery, we propose MS-Mamba-ATT, a multi-scale Mamba framework with hybrid global-spatial attention. The architecture integrates a hierarchical Mamba encoder, a multi-head attention bridge, a depth-aware 2D-to-3D lifting module, and a spatial-attention-guided decoder, regularized by a physics-guided loss to ensure forward-response consistency. Evaluations on the SEG Bishop benchmark synthetic model show that MS-Mamba-ATT significantly minimizes inversion errors and data residuals, outperforming existing U-Net and Transformer baselines. Furthermore, real-world validation on the Vinton salt dome via target-oriented fine-tuning yields excellent vertical gravity backfitting and robust 3D spatial consistency across six independent gravity-gradient tensor components, demonstrating an efficient and physically credible paradigm for intelligent potential field inversion.

Accurate segmentation of the pancreas and its lesions in CT scans is crucial for the precise diagnosis and treatment of pancreatic cancer. However, it remains a highly challenging task due to several factors such as low tissue contrast with surrounding organs, blurry anatomical boundaries, irregular organ shapes, and the small size of lesions. To tackle these issues, we propose DB-MSMUNet (Dual-Branch Multi-scale Mamba UNet), a novel encoder-decoder architecture designed specifically for robust pancreatic segmentation. The encoder is constructed using a Multi-scale Mamba Module (MSMM), which combines deformable convolutions and multi-scale state space modeling to enhance both global context modeling and local deformation adaptation. The network employs a dual-decoder design: the edge decoder introduces an Edge Enhancement Path (EEP) to explicitly capture boundary cues and refine fuzzy contours, while the area decoder incorporates a Multi-layer Decoder (MLD) to preserve fine-grained details and accurately reconstruct small lesions by leveraging multi-scale deep semantic features. Furthermore, Auxiliary Deep Supervision (ADS) heads are added at multiple scales to both decoders, providing more accurate gradient feedback and further enhancing the discriminative capability of multi-scale features. We conduct extensive experiments on three datasets: the NIH Pancreas dataset, the MSD dataset, and a clinical pancreatic tumor dataset provided by collaborating hospitals. DB-MSMUNet achieves Dice Similarity Coefficients of$89.47 \%, 87.59 \%$, and 89.02 %, respectively, outperforming most existing state-of-the-art methods in terms of segmentation accuracy, edge preservation, and robustness across different datasets. These results demonstrate the effectiveness and generalizability of the proposed method for real-world pancreatic CT segmentation tasks.

Due to the low contrast between lesion features and surrounding tissues in non-contrast CT images, traditional detection methods often struggle to accurately identify and differentiate various types of cystic tumors. This limitation increases the risk of misdiagnosis and missed detection, thereby hindering the clinical application of contrast-free techniques. To address this issue, we propose a detection framework that combines the teacher-student model with feature interaction as a non-contrast pancreatic cystic tumor detection technique. The pseudo-labeling of contrast-free CT is detected using the teacher network, which serves as supervisory information for the student network to guide learning. The student network transfers lesion feature information from contrast-enhanced CT to enrich the representation of lesion features. To avoid the confusion of features in multi-phase CT, a multi-domain discriminator is introduced for adversarial learning to extract modality-independent features, which significantly improves the robustness of the model. The experimental results show that the proposed method outperforms the traditional method on plain CT, can effectively detect SCN and MCN lesions, and provides a reliable contrast-free diagnostic solution for the clinic.

Domain generalization (DG) aims to enhance the ability of model learning from source domains to generalize to other unseen domains. Existing gradient-based methods focus on learning better domain-invariant features using gradients from multiple source domains, but do not consider the impact of gradient granularity on model training. In this paper, we rethink how to mitigate the gradient conflicting problem from an optimization perspective. The limitations of existing gradient-based methods are theoretically analyzed in terms of modification ratio and modification frequency, showing that gradient granularity is a key factor in ensuring correct modification of the gradient. To address this issue, a gradient modification method, called CorGrad, is proposed by layering and slicing refinement operations to increase the modification frequency and the modification ratio. It can better reduce domain-specific information so that the model can learn better domain-invariant features. Finally, extensive experiments are conducted to verify the effectiveness of the proposed CorGrad, and the results show that the proposed CorGrad can obtain competitive performance in five DG benchmarks, and an average performance of 60.4% can be obtained on the DomainBed when using ResNet18 as the backbone. The code is publicly available at https://github.com/libzwo/CorGrad.

For the few-shot segmentation (FSS) task, existing methods attempt to capture the diversity of new classes by fully utilizing the limited support images, such as cross-attention and prototype matching. However, they often overlook the fact that there is variability in different regions of the same object, and intra-image similarity is higher than inter-image similarity. To address these limitations, a Self-Guided Transformer (SGT) is proposed by leveraging intra-image similarity to improve intra-object inconsistencies in this paper. The proposed SGT can selectively guide segmentation, emphasizing the regions that are easily distinguishable while adapting to the challenges caused by less discriminative regions within objects. Through a refined feature interaction scheme and the novel SGT module, our method can achieve state-of-the-art performance on various FSS datasets, demonstrating significant advances in few-shot semantic segmentation. The code is publicly available at https://github.com/HuHaigen/SGT.

Effective lesion detection in medical image is not only rely on the features of lesion region, but also deeply relative to the surrounding information. However, most current methods have not fully utilize it. What’s more, multi-scale feature fusion mechanism of most traditional detectors are unable to transmit detail information without loss, which makes it hard to detect small and boundary-ambiguous lesion in early stage disease. To address the above issues, we propose a novel intra- and across-layer feature interaction FCOS model (IAFI-FCOS) with a multi-scale feature fusion mechanism ICAF-FPN, which is a network structure with intra-layer context augmentation (ICA) block and across-layer feature weighting (AFW) block. Therefore, the traditional FCOS detector is optimized by enriching the feature representation from two perspectives. Specifically, the ICA block utilizes dilated attention to augment the context information in order to capture long-range dependencies between the lesion region and the surrounding. The AFW block utilizes dual-axis attention mechanism and weighting operation to obtain the efficient across-layer interaction features, enhancing the representation of detailed features. Our approach has been extensively experimented on both the private pancreatic lesion dataset and the public DeepLesion dataset, with AP50of 62.2% and 60.0%, respectively, and these results are 6.4% and 2.3% higher than the FCOS. Additionally, our model achieves SOTA results on the pancreatic lesion dataset.

Person re-identification is challenging due to the presence of variations in pose and occlusion, which significantly impact the matching of visual features across different camera views and pose considerable difficulty for accurate person re-identification. This paper proposes a novel method for occluded person re-identification by introducing point-level feature learning based on vision transformers. Our approach utilizes a pose estimator to detect the keypoints of the human body and employs these points to locate intermediate features. These intermediate features of keypoints are input to a pose-based transformer branch to learn point-level features. Then, we design a part-based transformer branch to learn part-level features that capture visual features of different image parts, further enhancing the discriminative power of the learned features. Additionally, we employ a global branch to learn the global-level feature by treating the person's image as a single entity. Finally, we integrate point-level, part-level, and global-level features to represent a person's features. The experimental results on occluded and partial person re-identification datasets demonstrate the effectiveness of our proposed approach in improving re-identification. Our approach shows potential for improving person re-identification in scenarios with occlusion and pose variations.

With the prosperity of deep learning techniques, salient object detection in remote sensing images (RSI-SOD) is concomitantly in full flourishing. However, due to the inherent challenges such as uncertainty in object quantities and scales, cluttered backgrounds, and blurred edges arising from shadows, most current approaches struggle for salient feature learning with the aid of heavy model architecture, yet often result in barely satisfactory performance. Some methods compromise model complexity to improve efficiency, albeit with significantly degraded results. To earn a satisfactory balance of efficacy and efficiency, we propose a new network for RSI-SOD, namely SFANet, based on progressive semantic flow and uncertainty-aware refinement. Specifically, we design a global semantic enhancement block (GSEB) to reduce background interference and accurately localize salient objects of varying quantities and scales, which further consists of three modularized components, i.e., semantic extraction module (SEM), interscale fusion module (IFM), and deep semantic graph-inference module (DSGM). SEM together with IFM contributes to the effective aggregation of multi-scale contexts by extracting fused and progressive semantic cues. DSGM performs semantic inference to better localize salient objects with irregularities in scale and topological structure. Furthermore, we present an uncertainty-aware refinement module (URM) to recognize salient objects in cluttered backgrounds and effectively suppress shadows. Extensive experiments are conducted on three RSI-SOD datasets, from which superior results can be achieved by our SFANet, outperforming the other cutting-edge methods. The code is available at https://github.com/ZhengJianwei2/SFANet.

Recent advancements in medical image segmentation have demonstrated superior performance by combining Transformer and U-Net due to the Transformer’s exceptional ability to capture long-range semantic dependencies. However, existing approaches mostly replace or concatenate the Convolutional Neural Networks (CNNs) and Transformers in series, which limits the potential of their combination. In this paper, we introduce a dual-branch feature encoder, CTI-UNet, that effectively fuses the global representations and local features of the CNN and Transformer branches at different scales through bidirectional feature interaction. Our proposed method outperforms existing approaches on multiple medical datasets, demonstrating state-of-the-art performance. The code for CTI-UNet is publicly available at https://github.com/huhaigen/CTI-UNet.

High-resolution (HR) magnetic resonance (MR) images provide more detailed information for reliable diagnoses and quantitative medical image analyses. Deep convolutional neural networks (CNNs) have demonstrated their ability to effectively retrieve HR MR images from low-resolution (LR) MR images. However, most CNN-based super-resolution (SR) algorithms treat content and background information equally, ignoring the unique properties of MR images, such as low contrast, intricate tissue textures, and sparse backgrounds. We present a Wavelet Dual- Stream Network (WDN) for accurate MR SR that addresses the issues raised above. First, a wavelet transform is leveraged at the network's beginning to decouple the inputs, which are divided into high-frequency and low-frequency sub-bands. The high-frequency sub-bands relate to content information with larger frequency changes, and the low-frequency sub-bands correspond to background information. In addition, we devise a two-branch structure to reconstruct the high-frequency and low-frequency features separately. On the one hand, we design the U-Net Attention (U-A) mechanism for focusing the network's attention on regions with critical information. On the other hand, due to the correlation between high-frequency and low- frequency branches, We establish the Cross Attention Block (CAB) to accomplish the interaction between two branches. CAB takes advantage of the redundancy of information between different branches to distill the information of the current branch. Finally, the inverse wavelet transform is utilized to couple the modified high-frequency and low-frequency sub-bands as SR. Extensive experiments confirm the effectiveness of the WDN, which provides a clear improvement over the state-of-the-art method in both subjective and objective evaluations.

Recently, many salient object detection (SOD) methods decouple image features into body features and edge features, which imply a new development direction in the field of SOD. Most of them mainly focus on how to decouple features, but the fusion method for the decoupled features can be further improved. In this paper, we propose a network, namely Progressive Coupling and Refining Network (PCRNet), which allows the progressive coupling and refining of the decoupled features to get accurate salient features . Furthermore, a novel loss, namely Non-Binary Intersection over Union (NBIoU), is proposed based on the characteristics of non-binary label images and the principle of Intersection over Union (IoU) loss. Experimental results show that our NBIoU performance surpasses binary cross-entropy (BCE), IoU and Dice on non-binary label images. The results on five popular SOD benchmark datasets show that our PCRNet significantly exceeds the previous state-of-the-art (SOTA) methods on multiple metrics. In addition, although our method is designed for SOD, it is comparable with previous SOTA methods on multiple benchmark datasets for camouflaged object detection without any modification on the network structure, verified the robustness of the proposed method.

To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and rectified linear unit (ReLU). To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VggNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC, and COCO). To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta, and ADAM) and different recognition tasks such as classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision, and generalization, and it can surpass other popular methods such as ReLU and adaptive functions such as Swish in almost all experiments in terms of overall performance.

A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying precisely the boundary of each cell in a microscopic image. The cell segmentation task is still challenging due to the variable shapes, large size differences, uneven grayscale, and dense distribution of biological cells in microscopic images. In this paper, we propose a joint feature learning method that integrates the density and boundary branch into a multi-scale convolutional U-Net (MC-Unet). To enhance the supervision of cell density and boundary detection, the density and boundary loss is constructed to guide the joint learning of multiple features, where the density loss branch can address the challenges posed by high density, while the boundary loss branch can address the problems of unclear cell boundaries and partial cell occlusion. A series of experiments on different cell datasets show that two auxiliary branches improve the learning of features on cell density and cell boundaries and that the proposed method is effective on different segmentation models. The code is available at: https://github.com/HuHaigen/Joint-Feature-Learning-for-Cell-Segmentation.

Attention is one of the most valuable breakthroughs in the deep learning community, and how to effectively utilize the attention information of channel and spatial is still one of the hot research topics. In this work, we integrate the advantages of channel and spatial mechanism to propose a Channel-Spatial hybrid Attention Module (CSHAM). Specifically, max-average fusion Channel Attention Module and Spatial Attention Neighbor Enhancement Module are firstly proposed, respectively. Then the connection between the two modules is analyzed and designed, and an alternate connection strategy with the transformation of channel weights is proposed. The key idea is to repeatedly use the channel weight information generated by the channel attention module, and to reduce the negative impact of the network complexity caused by the addition of the attention mechanism. Finally, a series of comparison experiments are conducted on CIFAR100 and Caltech-101 based on various backbone models. The results show that the proposed methods can obtain the best Top-1 performance among the existing popular methods, and can rise by nearly 1% in accuracy while basically maintaining the parameters and FLOPs. The code is publicly available at https://github.com/HuHaigen/A-Channel-Spatial-Hybrid-Attention-Mechanism-using-Channel-Weight-Transfer-Strategy. The package includes the proposed CSHAM for reproducibility purposes.

Magnetic Resonance Images (MRIs) of different modalities have different reference values for pathological diagnosis. But it is difficult to obtain multimodality MRIs. So, medical image synthesis has been proposed as an effective solution, with which any missing modalities are synthesized from the existing ones. To train a multimodal MRI synthesizer with limited number of unpaired MRIs, in this paper, we have proposed a novel High-dimensional Knowledge Guided Generative Adversarial Network (HKG-GAN). In our HKG-GAN, a cross-dimensional knowledge transfer network is utilized to extract features from 2D images (slices of MRIs) to measure the perceptual similarity of images of source and synthesized modalities, whose knowledge is transferred from a pre-trained 3D network without accessing its private training dataset. Nevertheless, based on code-splitting and cross-decoding, HKG-GAN is a one-for-all network that encodes MRIs into content codes and style codes, and then cross-decodes the encoding of a random image of different modality to convert MRI to target modality. The effectiveness has been proofed through comparative experiments.

An activation function is a key component of artificial neural networks (ANNs). It has a great impact on the performance and convergence of neural networks. In this work, a self-adapting methodology is proposed to explore the optimal adaptive activation functions for various tasks based on S-shaped or ReLu-shaped activation functions, which are regulated only by introducing several parameters. To verify the effectiveness of the proposed methodology, a series of comparison experiments are performed with MLP, CNN and RNN network structure on the benchmark datasets of image, text and audio. The experimental results are encouraging, and show that the proposed methodology can locate the optimal activation functions for various tasks. Nevertheless, the obtained functions are competitive and the improvements on network performance are significant compared with other popular activation functions, such as ELU, PReLU, ReLU, and Sigmoid.

Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different life stages of bladder cells by using two cascade Convolutional Neural Networks (CNNs). Initially, a hybrid object proposal algorithm (called EdgeSelective) by combining EdgeBoxes and Selective Search is proposed to generate candidate object proposals instead of a single Selective Search method in Region-CNN (R-CNN), and it can exploit the advantages of different mechanisms for generating proposals so that each cell in the image can be fully contained by at least one proposed region during the detection process. Then, the obtained cells from the previous step are used to train and extract features by employing CNNs for the purpose of cell life stage recognition. Finally, a series of comparison experiments are implemented. The results show that the proposed method can obtain better performance than traditional methods either in the stage of cell detection or cell life stage recognition, and it encourages and suggests the application in the development of new anticancer drug and cytopathology analysis of cancer patients in the near future.

Due to the poor resolution and low signal-to-noise ratio in PET images, and especially to the wide variation in size, shape, site and SUV value among different patients or even for the same patient, lymphoma segmentation in 3D PET Images is still a challenging task in the field of medical image processing. In this work, a novel non-self background-based data enhancement method is proposed for the deep learning-based lymphoma segmentation problem. Firstly, a lymphoma pool with 1991 lymphoid lesions is created. Then, some lymphomas from the lymphoma pool are randomly selected and integrated into their non-self images of the patients according to their respective coordinates when training networks. Finally, a series of comparison experiments among various network models and methods are conducted to verify the effectiveness of the proposed method. The results indicated that the proposed method was promising, and could obtain better comprehensive performance than other methods without any data enhancements for the lymphoma segmentation problems.

Owing to the high density, low contrast, deformable cell shapes, low inter-cellular shape and appearance variation, and occlusion of the cells by division or fusion especially in phase-contrast microscopy images, it is still a challenging task to segment cells from the complex background. In this work, we proposed a multi-scale convolution Unet (MC-Unet) for bladder cancer cell segmentation in Phase-Contrast microscopy images. More specifically, the second 3x3 convolution of each layer in the standard Unet is replaced with a multi-scale convolution (MC) block with different kernel sizes, such as 1x1, 3x3, and 5x5. To verify the effectiveness of the proposed method, a series of experiments are conducted on the bladder cancer T24 dataset and the MoNuSeg dataset, and the results shows the proposed MC-Unet can obtain better comprehensive performance than the standard Unet.

Segmentation of lumen (LB) and outer wall boundaries (OB) of carotid artery in magnetic resonance (MR) images is essential for carotid atherosclerotic disease diagnosis. However, the limited image signal‐to‐noise ratio, flow artefact, and varied lumen and outer wall become significant obstacles for automatic segmentation. A fully automatic framework is proposed for LB and OB segmentation in MR images. First, the lumen is identified by the support vector machine using a special strategy and LB is segmented by the geodesic star‐shape‐constrained graph cut. Then a novel global optimisation is developed to segment OB based on the graph cut, which consists of shape priors and appearance priors. The shape priors are learned from labelled shapes on LB and OB, while the appearance priors are modelled by Gaussian mixture models. A novel shape constraint is also designed as the constraint term. To evaluate author's method, extensive experiments are carried out from 160 MR images belonging to 16 patients. Experimental results demonstrate that the proposed method can yield high accuracy with fully automatic segmentation. Moreover, the advantages of the proposed method have been shown in terms of high flexibility and accuracy without user interactions in comparison with other methods.

Human interactions and their action categories preserve strong correlations, and the identification of the interaction configuration is of significant importance to improve the action recognition result. However, interactions are typically estimated using heuristics or treated as latent variables. The former usually produces incorrect interaction configuration while the latter introduces challenging training problem. Hence we propose a framework to jointly learn interactions and actions by designing a potential function using both features learned via deep neural networks and human interaction context. We propose an iterative approach to solve the associated inference problem efficiently and approximately. Experimental results on real datasets demonstrate that the proposed approach outperforms baselines by a large margin, and is competitive compared with the state-of-the-arts.

A better understanding of human interactions in videos can be achieved by simultaneously considering the coarse interactions between people, the action of each individual, and the activity of all people as a whole. We divide the recognition task into two stages. The first stage discriminates interactions and noninteractions, actions and activities based on local image information, while during the second stage, actions and activities are recognized in a global manner based on the local recognition results. A conditional random field (CRF) is designed to model human interactions in the spatio-temporal space. Different from most existing global models which cover either action or activity variables only, our model covers them both by considering the interactions between different types of variables. The graph structure of the CRF is predicted by a model learned from training data, which is different from traditional graph construction methods that typically rely on human heuristics. We learn the parameters of the CRF via structured support vector machine. We propose an efficient inference algorithm to tackle the estimation of labels in long videos containing many people. Our model admits both semantic-level understanding of human interactions in videos and competitive action and activity recognition performance.

This paper proposes a method of constructing a dynamic model of a ventricle based on isogeometric simulation so as to diagnose cardiac disease more accurately. Isogeometric simulation is an accurate simulation technology based on NURBS, which has evolved into an essential tool for a semi-analytical representation of geometric entities. Especially, a new method of moving control points is used to achieve a dynamic model of the ventricle. This method promises the model to be very accurate, efficient, and successive, in comparison with traditional models. Furthermore, the paper also puts forward a new error estimation method, which adopts the vector norm to get an overall analysis of the error coefficient in each direction. The error estimation method avoids a complicated estimation for each knot. It can not only be used to evaluate the value of the error accurately, but also reduce the local error by adjusting the control points. Moreover, the proposed NURBS model can especially be useful to analyze the motion and dynamics of the heart, and it is important for doctors to find early cues of cardiac diseases.