Traditional unsupervised multi-source domain adaptation usually assumes that all source domain data can be utilized during training. Unfortunately, due to practical concerns such as privacy, data storage, and computational costs, data from different source domains are often isolated from each other. To address this issue, we propose a federated domain adaptation framework based on fine-grained alignment. This method achieves domain adaptation at the model level through iterative training of source and target domains, thereby avoiding the direct use of source domain data. Specifically, our approach employs specialized techniques at various stages—model construction, pseudo-label generation, and model training—to handle fine-grained features that are often overlooked. This enables the model to effectively remove irrelevant information and learn more discriminative features, thus narrowing the distribution gap between domains. Extensive experimental results demonstrate the effectiveness of our proposed method across multiple datasets.

In this paper, we investigate the Source-Free Domain Adaptation (SFDA), where a well-trained model adapts to an unlabeled target domain without access to source data. Previous SFDA methods mainly relied on convolutional neural networks, which struggle with domain shifts due to their local focus. To address this, we propose the Object-centric Perception Source-Free Transformer (OP-SFT), which leverages the self-attention mechanism of Transformers to focus on relevant target regions, improving adaptability to domain shifts. We also introduce self-supervised knowledge distillation to enhance semantic perception and a confidence-based k-means clustering method for more accurate pseudo-label generation. Extensive experiments demonstrate that our OP-SFT achieves significant adaptation performance across four widely-used domain adaptation benchmark datasets compared to other state-of-the-art baselines. The code is available at https://github.com/Weilong-Gao/OP-SFT.

Multi-source Domain Adaptation (MSDA) aims to adapt models trained on multiple labeled source domains to an unlabeled target domain. Recent MSDA methods based on Generative Adversarial Networks (GANs) implicitly capture the image distribution, which can lead to limited sample fidelity and result in misalignment of pixel-level information between the sources and the target domain. Moreover, when samples from different sources interact during training, significant misalignment across various source domains can occur. In this study, we introduce a novel MSDA framework called Adaptive Causal Diffusion Networks (ACDN) to address these challenges. ACDN integrates a diffusive domain adaptation model for effective, high-fidelity adaptation between the source and target domains, incorporating Granger-causal inference to ensure that the assigned weights for each source domain are closely related to their respective contributions to the decision-making process. Experimental results show that ACDN outperforms existing methods significantly across real-world domain adaptation benchmarks.

U-Net architectures have made great progress in dealing with semantic segmentation tasks. However, existing frameworks have not yet possessed the ability of capturing sufficient local and contextual dependencies of tubular structures. The reasons are two-fold. First, traditional square convolutions are inherently limited to model irregular pixel changes due to their fixed geometric structures. Second, there exist semantic gaps among the multi-scale tubularity features and between stages of their encoding and the decoding. To mitigate these issues, we propose multi-scale tubularity-aware U-Net, by coupling a novel tubularity deformable convolution (TdConv) embedding and a dual attention Transformer (DaTrans) alternative to skip connection. On the one hand, TdConv embedding iteratively learns the deformation offsets of convolution itself in both directions along the tubular structure. On the other hand, DaTrans connection endows skip connections with attention mechanism from both multi-scale local pixel and cross-scale global semantic perspectives. Hinging on the local irregularity perception and the global semantic association, our method enables to analyze tubular structures appeared in complex contexts and at different scales. Extensive experiments show that our approach outperforms state-of-the-art techniques, including different U-Net variants, for various datasets on several tasks including road extraction and vessel segmentation.

Automated road extraction from satellite imagery for dynamic map updating has become a crucial research focus in remote sensing, where existing state-of-the-art Transformer-based methods exhibit two critical limitations: (1) inadequate precision in capturing tubular road patterns and (2) suboptimal computational efficiency on standard GPUs. To address these challenges, we propose TrMamba, a new Tracking-based Mamba architecture that combines the original Mamba’s efficiency with enhanced tubular road pattern recognition through two key innovations: a tubular road pattern tracking mechanism for continuous road feature extraction and a tracking selective scanning module for directional context modeling via adaptive attention. By integrating a novel tubular tracking mechanism into the Mamba’s selective scanning process, TrMamba fundamentally improves the original paradigm and achieves superior road topology encoding, as demonstrated by extensive experiments showing state-of-the-art performance across multiple remote sensing benchmarks in both accuracy and computational efficiency. The source code is available at: https://github.com/Apheliosa/TrMamba.

The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our method, called DUSA-UNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. DUSA-UNet offers a novel alternative to the U-Net architecture by integrating connectivity attention, which can exploit intra-road interactions across multi-level sampling features with reduced computational complexity. This local interaction serves as valuable prior information for learning global interactions between road networks and the background through another integrality attention mechanism. The two forms of sparse attention are arranged alternatively and complementarily, and trained jointly, resulting in performance improvements without significant increases in computational complexity. Extensive experiments on various datasets with different resolutions, including Massachusetts, DeepGlobe, SpaceNet, and Large-Scale remote sensing images, demonstrate that DUSA-UNet outperforms state-of-the-art techniques. Our approach represents a significant advancement in the field of road network extraction, providing a computationally feasible solution that achieves high-quality segmentation results.

Precisely knowing each instance’s position and extents is a critical first step in many biological applications. State-of-the-art techniques rely either on deep learning models designed to predict segmentation masks on each Region of Interest (RoI) or on classic active contour methods. The former struggles to precisely delineating boundaries and tends to output masks at low resolutions when the cells/nuclei are very irregular while the latter often needs good initialization and manual setting of parameters, thus limiting their usefulness. To bridge this gap, we introduce Snake R-CNN, a new level of the learnable active contour model that predict boundary on each RoI in a sequent way. To do so, for each RoI, we reformulate the contour deformation task in terms of a hidden state evolution problem and update the evolution process using energy minimization. We learn snake parameterizations per instance in an end-to-end manner, and demonstrate its effectiveness for contour inferences of various cell/nucleus types where consistently higher performances were obtained for comparison against state-of-the-arts.

Open-Curve Snake (OCS) has been successfully used in three-dimensional tracking of neurites. However, it is limited when dealing with noise-contaminated weak filament signals in real-world applications. In addition, its tracking results are highly sensitive to initial seeds and depend only on image gradient-derived forces. To address these issues and boost the canonical OCS tracker to a new level of learnable deep learning algorithms, we present Deep Open-Curve Snake (DOCS), a novel discriminative 3D neuron tracking framework that simultaneously learns a 3D distance-regression discriminator and a 3D deeply-learned tracker under the energy minimization, which can promote each other. In particular, the open curve tracking process in DOCS is formed as convolutional neural network prediction procedures of new deformation fields, stretching directions, and local radii and iteratively updated by minimizing a tractable energy function containing fitting forces and curve length. By sharing the same deep learning architectures in an end-to-end trainable framework, DOCS is able to fully grasp the information available in the volumetric neuronal data to address segmentation, tracing, and reconstruction of complete neuron structures in the wild. We demonstrated the superiority of DOCS by evaluating it on both the BigNeuron and Diadem datasets where consistently state-of-the-art performances were achieved for comparison against current neuron tracing and tracking approaches. Our method improves the average overlap score and distance score about 1.7% and 17% in the BigNeuron challenge data set, respectively, and the average overlap score about 4.1% in the Diadem dataset.

We study Universal Domain Adaptation (UniDA) problem, which is recently proposed. Different from existing domain adaptation (DA) methods, e.g., Closed set, Open set and Partial DA, UniDA does not need any prior knowledge about the overlap across the source and target label sets. We have two challenges in UniDA problem: i) Domain shift. ii) Category shift. Towards tackling above challenges, we formulate a universal adaptation network called Dual Contrastive Network (DCN), where a contrastive module and a transferability rule are included. The experimental results reveal that DCN can work stably on different UniDA settings and exceeds the state-of-the-art performance across five benchmarks against existing DA methods.

Automating generalized nucleus segmentation has proven to be non-trivial and challenging in digital pathology. Most existing techniques in the field rely either on deep neural networks or on shallow learning-based cascading models. The former lacks theoretical understanding and tends to degrade performance when only limited amounts of training data are available while the latter often suffers from limitations for generalization. To address these issues, we propose sparse coding driven deep decision tree ensembles (ScD2TE), an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in the generalized nucleus segmentation task. We explore the possibility of stacking several layers based on fast convolutional sparse coding–decision tree ensemble pairwise modules and generate a layer-wise encoder–decoder architecture with intra-decoder and inter-encoder dense connectivity patterns. Under this architecture, all the encoders share the same assumption across the different layers to represent images and interact with their decoders to give fast convergence. Compared with deep neural networks, our proposed ScD2TE does not require back-propagation computation and depends on less hyper-parameters. ScD2TE is able to achieve a fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the superiority of our segmentation method by evaluating it on the multi-disease state and multi-organ dataset where consistently higher performances were obtained for comparison against other state-of-the-art deep learning techniques and cascading methods with various connectivity patterns.

Generally, the traditional supervised hyperspectral image (HSI) classification cannot fully exploit spatial and spectral features simultaneously. In this paper, we reformulate HSI feature learning in terms of sparse separable convolutional filter learning problem and propose a sparse separable convolutional classification model (SSCCM). In the proposed SSCCM, the sparse separable convolutional learning module (SSCLM) is used to extract robust spatial-spectral features and utilizes rank-one tensor decomposion learning mechanism to accelerate feature computation. While the SVM classification module (SVMCM) employs the 3D spatial-spectral feature array to represent the HSI for classification. Experimental results on the widely used HSI datasets demonstrate the superior performance of our proposed approach over the state-of-theart classification methods.

In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of the cell carcinoma and gene expression. The focus of the past literature is dominated by either segmenting a certain type of cells/nuclei or simply splitting the clustered objects without contours inference of them. Our method addresses these issues by formulating the detection and segmentation tasks in terms of a unified regression problem, where a cascade sparse regression chain model is trained and then applied to return object locations and entire boundaries of clustered objects. In particular, we first learn a set of online convolutional features in each layer. Then, in the proposed cascade sparse regression chain, with the input from the learned features, we iteratively update the locations and clustered object boundaries until convergence. In this way, the boundary evidences of each individual object can be easily delineated and be further fed to a complete contour inference procedure optimized by the minimum description length principle. For any probe image, our method enables to analyze free-lying and overlapping cells with complex shapes. Experimental results show that the proposed method is very generic and performs well on contour inferences of various cell/nucleus types. Compared with the current segmentation techniques, our approach achieves state-of-the-art performances on four challenging datasets, i.e., the kidney renal cell carcinoma histopathology dataset, Drosophila Kc167 cellular dataset, differential interference contrast red blood cell dataset, and cervical cytology dataset.

This paper presents a novel method for automated morphology delineation and analysis of cell nuclei in histopathology images. Combining the initial segmentation information and concavity measurement, the proposed method first segments clusters of nuclei into individual pieces, avoiding segmentation errors introduced by the scale-constrained Laplacian-of-Gaussian filtering. After that a nuclear boundary-to-marker evidence computing is introduced to delineate individual objects after the refined segmentation process. The obtained evidence set is then modeled by the periodic B-splines with the minimum description length principle, which achieves a practical compromise between the complexity of the nuclear structure and its coverage of the fluorescence signal to avoid the underfitting and overfitting results. The algorithm is computationally efficient and has been tested on the synthetic database as well as 45 real histopathology images. By comparing the proposed method with several state-of-the-art methods, experimental results show the superior recognition performance of our method and indicate the potential applications of analyzing the intrinsic features of nuclei morphology.