Downsampling is essential in semantic segmentation for reducing computational cost and guiding the learning of class-discriminative features. Existing models typically rely on strided convolutions or patch splitting to obtain features with lower resolution. However, we observe that such operations often introduce edge jagging and texture degradation, the underlying cause is that aliasing of the high frequency induces phase distortion. We conducted a systematic analysis of phase distortion and identified two key properties: spatial non-uniformity (concentrated near boundaries) and directional sparsity (accumulated along a few dominant directions). These properties cause crucial high-frequency cues to be misrepresented or lost during sampling. To address this issue, we propose a frequency aware filter consisting of two complementary modules: a dynamic Gaussian kernel (DGK) and a learnable Gabor-based frequency selector (LFS). To mitigate spatial non-uniformity, the DGK predicts edge normals from gradients, applies strong low-pass filtering along the normal direction, and leaves the tangential direction virtually untouched, thereby suppressing phase distortion while preserving contour continuity. To handle directional sparsity, the Learnable Gabor Selector (LFS) then performs directional band-pass filtering to attenuate residual aliasing peaks and adaptively boost informative texture. We further introduce phase-error energy (PE) to quantify distortion severity. Visualization and quantitative results demonstrate that frequency-aware filter offers a plug-and-play remedy for aliasing, yielding sharper boundaries and consistent gains across datasets.

Recently, diffusion-based image compression has achieved significant progress in terms of rate–distortion-perception trade-off, these approaches have replaced decoders with conditional diffusion models to enhance the visual quality of reconstructed images. However, diffusion models introduce noise into the input image during the initial stages of the diffusion process, which may cause the potential degradation of crucial image information. To address these limitations, we propose a Hierarchical Conditional guidance Diffusion model for perceptual Image Compression (HCD-IC) to ensure the fidelity of reconstruction, in which hierarchical features with selected typical context provide informative guidance during the denoising process of diffusion model to preserve both structural integrity and fine details. Specifically, we design a Gated Scale-Cross module (GSC) to integrate and select representative semantics and details, which leverages a hierarchical feature interaction architecture and dynamic gated strategy to ensure more robust and expressive representations. Furthermore, we present a Conditional Control Diffusion decode module (CCD) to integrate time-step information and latent features augmented by GSC into the diffusion model, which can dynamically acquire the required time-aware conditional features during different denoise stages. Extensive experiments conducted on multiple public datasets demonstrate that our method outperforms state-of-the-art approaches in various quantitative realism metrics.

Conventional Self-Supervised Learning (SSL) exhibits notable limitations in fine-grained feature modeling due to pervasive issues in medical imaging, such as blurred organ boundaries, complex anatomical structures, and feature confusion caused by similar pathological patches, often leading to false positive sample interference. To address these challenges, this article proposes Gaze-Guided Semantic Contrastive Learning (G2Co), an innovative SSL algorithm inspired by visual diagnostic patterns of radiologists. At the semantic enhancement level, G2Co leverages a key information guidance mechanism to distinguish anatomical structures from background noise, thereby achieving fine-grained feature extraction. At the feature interaction level, G2Co introduces a cross-sample feature fusion strategy to extract discriminative features from potential positive samples, addressing feature confusion caused by visually similar patches. Furthermore, G2Co achieves refined modeling of tissue morphology and boundary characteristics by establishing inter-region mutual information maximization constraints. Finally, extensive experiments are conducted on the two widely used medical image datasets to demonstrate the effectiveness of our method.

Remote sensing change detection(RSCD) aims to identify the regions of interest that have changed between dual-temporal images. However, most deep models predict CD results by extracting multi-scale hybrid features, which could easily lead to ambiguous semantic boundaries; in addition, the existing feature acquisition tends to lack consideration of capturing their diversity usually causing poor model generalization. Thus, this paper decomposes the mixed features into change and invariant features jointly with stochastic distribution sampling and convolution thus accomplishing robust RSCD based on decoupled representations. In the training stage, the posterior distribution of the uncoupled features is first learned through label calibration to train the prior distribution generator; then, robust feature decoupling is implemented combining the convolutional feature separator with re-parameterized sampling over the decoupled posteriori distribution, and further aggregating the decoupled features through prototype learning; finally, the exceed-expectation loss regularizer is proposed to push or pull these positive and negative sample features to a more distant end, thereby increasing the inter-class distance by boosting the predicted expectation. In the testing stage, the robust RSCD based on decoupled representation is accomplished through the feature separator, decoupled prior distribution random sampling, and CD head without posterior distribution support. Experiments prove that DSFDcd has achieved remarkable results in terms of qualitative and quantitative metrics. Our codes will be available at https://github.com/iceking111/DSFDcd.

Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning, and post-operative assessment. With the rapid advancement of deep learning technologies, the development of numerous models based on convolutional neural networks and transformer-based models has been applied to the precise segmentation of WSIs. However, due to privacy regulations and the need to protect patient confidentiality, centralized storage and processing of image data are impractical. Training a centralized model directly is challenging to implement in medical settings due to these privacy concerns.This paper addresses the dispersed nature and privacy sensitivity of medical image data by employing a federated learning framework, allowing medical institutions to collaboratively learn while protecting patient privacy. Additionally, to address the issue of original data reconstruction through gradient inversion during the federated learning training process, differential privacy introduces noise into the model updates, preventing attackers from inferring the contributions of individual samples, thereby protecting the privacy of the training data.Experimental results show that the proposed method, FedDP, minimally impacts model accuracy while effectively safeguarding the privacy of cancer pathology image data, with only a slight decrease in Dice, Jaccard, and Acc indices by 0.55%, 0.63%, and 0.42%, respectively. This approach facilitates cross-institutional collaboration and knowledge sharing while protecting sensitive data privacy, providing a viable solution for further research and application in the medical field.

Abstract Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes. Therefore, accurate classification of cancer subtypes can help doctors choose the most appropriate treatment options, improve treatment outcomes, and provide more accurate patient survival predictions. In this study, we propose a supervised multi-head attention mechanism model (SMA) to classify cancer subtypes successfully. The attention mechanism and feature sharing module of the SMA model can successfully learn the global and local feature information of multi-omics data. Second, it enriches the parameters of the model by deeply fusing multi-head attention encoders from Siamese through the fusion module. Validated by extensive experiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1 weighted, and accurate classification of cancer subtypes in simulated, single-cell, and cancer multi-omics datasets compared to AE, CNN, and GNN-based models. Therefore, we contribute to future research on multi-omics data using our attention-based approach.

Data‐hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data‐hunger in machine‐learning‐based image‐processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data‐hunger. Based on these findings, this paper introduces a novel semi‐white‐box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all‐in‐one model, a semi‐white‐box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well‐established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi‐white‐box strategy over the traditional all‐in‐one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi‐white‐box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi‐white‐box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all‐in‐one approach. In conclusion, this paper contributes to mitigating data‐hunger challenges in machine‐learning‐based image processing through the introduction of a novel semi‐white‐box model construction strategy, backed by empirical evidence of its effectiveness.

Compared to computed tomography (CT), magnetic resonance imaging (MRI) is more sensitive to acute ischemic stroke lesion. However, MRI is time-consuming, expensive, and susceptible to interference from metal implants. Generating MRI images from CT images can address the limitations of MRI. The key problem in the process is obtaining lesion information from CT. In this study, we propose a cross-modal image generation algorithm from CT to MRI for acute ischemic stroke by combining radiomics with generative adversarial networks. First, the lesion candidate region was obtained using radiomics, the radiomic features of the region were extracted, and the feature with the largest information gain was selected and visualized as a feature map. Then, the concatenation of the extracted feature map and the CT image was input in the generator. We added a residual module after the downsampling of the generator, following the general shape of U-Net, which can deepen the network without causing degradation problems. In addition, we introduced the lesion feature similarity loss function to focus the model on the similarity of the lesion. Through the subjective judgment of two experienced radiologists and using evaluation metrics, the results showed that the generated MRI images were very similar to the real MRI images. Moreover, the locations of the lesions were correct, and the shapes of lesions were similar to those of the real lesions, which can help doctors with timely diagnosis and treatment.

Aiming at the problem of insufficient detail retention in multimodal medical image fusion (MMIF) based on sparse representation (SR), an MMIF method based on density peak clustering and convolution sparse representation (CSR-DPC) is proposed. First, the base layer is obtained based on the registered input image by the averaging filter, and the original image minus the base layer to obtain the detail layer. Second, for retaining the details of the fused image, the detail layer image is fused by CSR to obtain the fused detail layer image, then the base layer image is segmented into several image blocks, and the blocks are clustered by using DPC to obtain some clusters, and each class cluster is trained to obtain a sub-dictionary, and all the sub-dictionaries are fused to obtain an adaptive dictionary. The sparse coefficient is fused through the learned adaptive dictionary, and the fused base layer image is obtained through reconstruction. Finally, fusing the detail layer and the base layer and reconstructing them forms the ultimate fused image. Experiments show that compared to the state-of-the-art two multi-scale transformation methods and five SR methods, the proposed method(CSR-DPC) outperforms the other methods in terms of the image details, the visual quality and the objective evaluation index, which can be helpful for clinical diagnosis and adjuvant treatment.

Ultrasonography is one of the main imaging methods for diagnosing thyroid nodules. Automatic differentiation between benign and malignant nodules in ultrasound images can greatly assist inexperienced clinicians in their diagnosis. The key of problem is the effective utilization of the features of ultrasound images. In this study, we propose a method that is based on the combination of conventional ultrasound and ultrasound elasticity images based on a convolutional neural network and introduces richer feature information for the classification of benign and malignant thyroid nodules. First, the conventional network model performs pretraining on ImageNet and transfers the feature parameters to the ultrasound image domain by transfer learning so that depth features may be extracted and small samples may be processed. Then, we combine the depth features of conventional ultrasound and ultrasound elasticity images to form a hybrid feature space. Finally, the classification is completed on the hybrid feature space, and an end-to-end CNN model is implemented. The experimental results demonstrate that the accuracy of the proposed method is 0.9470, which is better than that of other single data-source methods under the same conditions.

The world has entered into a “smart” era. One area becoming smart is the place where we live-homes. Smart homes are expected to be equipped with numerous sensors to continually monitor, sense, and actuate the space. The data from these sensors can be used to provide various types of services by automating common tasks while causing minimal disruption to daily life. In order to provide these services, a system must have sufficient intelligence to predict future events based on its observations. This paper first examines the requirements for smart home predictions. It then comprehensively reviews prediction algorithms and variations that have been proposed and investigated in smart environments, such as smart homes. It is these prediction algorithms that provide the intelligence required by a smart home. Comparisons are also made upon these prediction algorithms on their features and models.