Transformer-based segmentation methods exhibit considerable potential in medical image analysis. However, their improved performance often comes with increased computational complexity, limiting their application in resource-constrained medical settings. Prior methods follow two independent tracks: (i) accelerating existing networks via semantic-aware routing, and (ii) optimizing token adapter design to enhance network performance. Despite directness, they encounter unavoidable defects (e.g., inflexible acceleration techniques or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To address these shortcomings, we integrate these schemes by proposing the semantic-aware adapter (SarAdapter), which employs a semantic-based routing strategy, leveraging neural operators (ViT and CNN) of varying complexities. Specifically, it merges semantically similar tokens volume into low-resolution regions while preserving semantically distinct tokens as high-resolution regions. Additionally, we introduce a Mixed-adapter unit, which adaptively selects convolutional operators of varying complexities to better model regions at different scales. We evaluate our method on four medical datasets from three modalities and show that it achieves a superior balance between accuracy, model size, and efficiency. Notably, our proposed method achieves state-of-the-art segmentation quality on the Synapse dataset while reducing the number of tokens by 65.6%, signifying a substantial improvement in the efficiency of ViTs for the segmentation task.

Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the use of unpaired multi-modal data during the training phase, and relies on the widespread fundus photographs for testing. To benchmark this setting, we present the first large multi-modal multi-class dataset for eye disease diagnosis, MultiEYE, and propose an OCT-assisted Conceptual Distillation Approach (OCT-CoDA), which employs semantically rich concepts to extract disease-related knowledge from OCT images and leverages them into the fundus model. Specifically, we regard the image-concept relation as a link to distill useful knowledge from OCT teacher model to fundus student model, which considerably improves the diagnostic performance based on fundus images and formulates the cross-modal knowledge transfer into an explainable process. Through extensive experiments on the multi-disease classification task, our proposed OCT-CoDA demonstrates remarkable results and interpretability, showing great potential for clinical application. Our dataset and code are available at https://github.com/xmed-lab/MultiEYE.

We consider an information dissemination problem where the root node in an undirected graph constantly updates its information. The goal is to keep every other node in the graph as freshly informed about the root as possible. Our synchronous information spreading model uses telephone calls at each time step, in which any node can communicate with at most one neighbor, thus forming a matching over which information is transmitted at each step. We introduce two problems in minimizing two natural objectives (Maximum and Average) of the latency of the root's information at all nodes in the network. After deriving a simple reduction from the maximum rooted latency problem to the well-studied minimum broadcast time problem, we focus on the average rooted latency version. We introduce a natural problem of finding a finite schedule that minimizes the average broadcast time from a root. We show that any average rooted latency scheme induces a solution to this average broadcast problem within a constant factor and conversely, this average broadcast time is within a logarithmic factor of the average rooted latency. Then, we derive a log-squared approximation algorithm for the average broadcast time problem via rounding a time-indexed linear programming relaxation, resulting in a log-cubed approximation for the average latency problem. Surprisingly, we show that using the average broadcast time for average rooted latency introduces a necessary logarithmic factor overhead even in trees. We overcome this hurdle and give a 40-approximation for trees. For this, we design an algorithm to find near-optimal locally-periodic schedules in trees where each vertex receives information from its parent in regular intervals. On the other side, we show how such well-behaved schedules approximate the optimal schedule within a constant factor. * This material is based upon work supported in part by the U. S. Office of Naval Research under award number N00014-21-1-2243 and the Air Force Office of Scientific Research under award number FA9550-20-1-0080.

BACKGROUND: Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. RESULTS: To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis. CONCLUSION: The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .

The selection of training samples is important for the accuracy and efficiency of the synthetic aperture radar (SAR) image change detection task. However, training samples are traditionally extracted from the whole image, which leads to longer training time and an unbalanced number of pixels in the changed and unchanged classes. To overcome this problem, we propose a novel change detection method combining saliency detection with a principal component analysis network, named SDPCANet. To enhance the reliability of the training samples and reduce the amount of training samples, the SDPCANet uses context-aware saliency detection to obtain the salient region, from which the training samples are extracted. In addition, to alleviate the gap between the numbers of training samples in two classes, we regulate the candidate samples using the uniform-selecting strategy to enhance the reliability of the training samples for the SDPCANet. Then, the SDPCANet is trained with the extracted training samples and the remaining pixels are classified in the salient region to obtain the final change map. The experimental results on four sets of multitemporal SAR images demonstrate that the SDPCANet outperforms the reference methods proposed recently.

The WGΓ model has been validated as an effective model for the characteristic of polarimetric synthetic aperture radar (PolSAR) data statistics. However, due to the complexity of natural scene and the influence of coherent wave, the WGΓ model still needs to be improved to fully consider the polarimetric information. Then, we propose the WGΓ mixture model (WGΓMM) for PolSAR data to maintain the correlations among statistics in PolSAR data. To further consider the spatial-contextual information in PolSAR image classification, we propose a novel mixture model, named mixture WGΓ-Markov random field (MWGΓMRF) model, by introducing the MRF to improve the WGΓMM model for classification. In each law of the MWGΓ-MRF model, the interaction term based on the edge penalty function is constructed by the edge-based multilevel-logistic model, while the likelihood term being constructed by the WGΓ model, so that each law of the MWGΓ-MRF model can achieve an energy function and has its contribution to the inference of attributive class. Then, the mixture energy function of the MWGΓ-MRF model has the fusion of the weighted component, given the energy functions of every law. The mixture coefficient and the corresponding mean covariance matrix of the MWGΓ-MRF model are estimated by the expectation-maximization algorithm, while the parameters of the WGΓ model being estimated by the method of matrix log-cumulants. Experiments on simulated data and real PolSAR images demonstrate the effectiveness of the MWGΓ-MRF model and illustrate that it can provide strong noise immunity, get smoother homogeneous areas, and obtain more accurate edge locations.

Hidden Markov fields have been extensively applied in the field of synthetic aperture radar (SAR) image processing, mainly for segmentation and change detection. In such models, the hidden process of interest X is assumed to be a Markov field that is to be searched from an observable process Y. The possibility of such estimation lies, however, on several assumptions that turn out to be unsuitable for many natural systems. These models have then been extended in many directions, leading to triplet Markov fields among other extensions. A link has then been established between these models and the theory of evidence, opening new possibilities of uncertainty modeling and information fusion. The aim of this letter is to further generalize the hidden evidential Markov field (EMF) to consider more general forms of noise with application to unsupervised segmentation of SAR images. For parameters estimation, we use iterative conditional estimation, whereas maximization is performed through iterative conditional mode. The performance of the proposed model is assessed against the original EMF on real SAR images.

How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.

In this letter, we propose a hybrid conditional random field (HCRF) model for synthetic aperture radar (SAR) image change detection. The HCRF model is constructed by incorporating the statistics of the log-ratio image derived from the two-temporal SAR images into conditional random field model. In this way, it is able to integrate the SAR images information, including the texture features of the two-temporal SAR images, the statistics, and the spatial interactions of the log-ratio image, into the change detection. Moreover, to achieve the integration of the information, the HCRF model consists of three parts, namely, the unary potential, the pairwise potential, and the data term modeled by the statistics of the log-ratio image. The unary potential is modeled by a support vector machine using the texture features extracted from the two-temporal SAR images, and the pairwise potential is constructed by the multilevel logistical model to capture the spatial interactions of the log-ratio image. Generalized Gamma distribution (GΓD) is utilized to model the statistics of the intensity data in the log-ratio image. Finally, experimental results on three sets of two-temporal SAR images validate the effectiveness of the proposed HCRF model.

Statistical modeling for the statistics of polarimetric synthetic aperture radar (SAR) data is a critical factor in polarimetric SAR data processing. In this letter, we utilize the complex Wishart-generalized Gamma (WGΓ) distribution to model multilook polarimetric SAR data, in which the complex Wishart distribution and generalized Gamma distribution model the speckle and texture components, respectively. Moreover, we derive a closed-form expression for the WGΓ distribution based on the product model and propose a parameter estimation technique of the WGΓ distribution in this letter. We perform the experiments on the polarimetric SAR data acquired by the AIRSAR and ESAR to verify the superiority and effectiveness of the WGΓ distribution over the K and KummerU distributions in the goodness of fit of polarimetric SAR data histograms and the polarimetric SAR image classification. The experimental results demonstrate that the WGΓ distribution has a greater flexibility than the K and KummerU distributions in the statistical modeling of multilook polarimetric SAR data.

Kernel methods with specifically designed kernel function are suitable for dealing with practical nonlinear problems. However, kernel methods have found limited applications to synthetic aperture radar (SAR) image change detection in that their performances are affected by the inherent multiplicative speckle noise of SAR images. It is known that the spatial-contextual information is helpful in suppressing the degrading effects of the noise. Therefore, a label-information composite kernel (LIC kernel) constructed on the basis of the spatial-contextual information is proposed in this paper for SAR image change detection. A typical spatial information, the output-space label-neighborhood information that is extracted using all labels in the neighborhood of each pixel, may enhance noise immunity, but with inaccurate edge locations simultaneously. Consequently, the anisotropic Gaussian kernel model is utilized for analyzing anisotropic textures of the bitemporal images, and then, a comparison scheme acting on the input-space textures of the bi-temporal images is proposed to supervise the extraction of the output-space label-neighborhood information in the construction of the LIC kernel. The constructed LIC kernel is of good preservation of edge locations of changed areas as well as strong noise immunity. The LIC kernel is updated iteratively with the newest change map outputted from the support vector machine, until the change map converges. Experiments on real SAR images demonstrate the effectiveness of the LIC kernel method and illustrate that it has both strong noise immunity and good preservation of edge locations of changed areas for SAR image change detection.

Change detection can be performed in a supervised manner. However, supervised methods for synthetic aperture radar (SAR) image change detection may suffer from lack of training samples. Therefore, in this letter, a semisupervised support vector machine classifier based on a cluster-neighborhood (CN) kernel is proposed for SAR image change detection. In the proposed method, samples are categorized into two neighborhoods with kernel k-means clustering algorithm. In addition, a CN kernel is constructed based on the composite-ratio kernel using the neighborhood-based statistical features. When a few labeled samples are available, the proposed CN kernel explores the information of unlabeled samples to enhance its discriminative ability and enhance its robustness against speckles. Experimental results on real SAR image change detection demonstrate the effectiveness of the proposed method when a few labeled samples are available.