Magnetic particle imaging (MPI) has demonstrated its advantages of high sensitivity and temporal resolution in various preclinical applications. However, during the imaging process, the signal is susceptible to different noises, resulting in severe stripe artifacts in reconstructed MPI images. This phenomenon will be further aggravated in scenarios with low-concentration particles, which is a standard practice in biological applications, thereby seriously hindering the identification of key information. To solve this problem, we propose a joint optimization approach called diffusion denoising model for MPI (DDMPI) that integrates diffusion model with Transformer to remove the artifacts directly from MPI images obtained in the low-concentration scenarios. In DDMPI, a latent encoder generates prior features containing the relevance mapping between the contents and the artifacts within MPI images, and a conditional latent diffusion model optimizes these prior features. A U-shape Transformer module incorporates the prior features by a hierarchical integration module and utilizes a strip-self-attention module to capture the spatial distribution of the artifacts. Ablation experiments demonstrate the effectiveness of these modules in DDMPI. Extensive experiments, including simulation, phantom and in vivo experiments, demonstrate that DDMPI effectively removes artifacts and recovers fine details. Additionally, DDMPI is independent of the primary image reconstruction methods of various scanning devices. Thus, DDMPI can be not only practically applied to in vivo imaging but also flexibly combined with various existing MPI devices to effectively improve the imaging quality and provide critical information about diseases.

Histopathological analysis constitutes the diagnostic cornerstone in disease characterization, employing diverse staining methodologies to elucidate tissue architecture. While hematoxylin and eosin (H&E) remains the foundational technique, ancillary modalities, including specialized histochemical stains, immune-histochemistry (IHC), and multiplex immune-fluorescence (mpIF), yield critical complementary data essential for comprehensive diagnosis. Nevertheless, sequential implementation of these techniques necessitates protracted processing times, substantial labor investment, and significant tissue consumption, often requiring serial sectioning with iterative staining procedures that compromise sample integrity. To address these challenges, we propose StainExpert, a unified multimodal diffusion framework for source-to-multi-target pathological stain translation. Unlike existing approaches that require separate models for each staining pair, StainExpert establishes the first multi-expert system where specialized networks collaboratively learn staining principles while maintaining domain-specific expertise. Through multi-expert and multi-objective optimization, it enables efficient translation from a single source to multiple targets. Additionally, our multimodal diffusion architecture integrates textual guidance with visual features, achieving superior accuracy and pathology-informed translation. Leveraging parameter-efficient design and model distillation, StainExpert matches GAN-level efficiency while delivering superior generation quality. We validate StainExpert across three datasets spanning H&E, special stains, IHC, and mpIF modalities. Extensive evaluation demonstrates that StainExpert generates high-quality virtual stains that preserve critical pathological features for accurate diagnosis. Beyond robust cross-domain generalization, StainExpert offers a transformative platform for efficient multi-target stain translation, advancing toward streamlined, tissue-conserving, and resource-efficient diagnostic workflows in computational pathology. The code is available at https://rowerliu.github.io/StainExpert.

Generative models hold promise in addressing data scarcity and imbalance in computational pathology, yet current approaches often suffer from limited generalization due to either overfitting on narrow domains or reliance on pre-trained models from unrelated natural image distributions. In this work, we introduce OptiPathD, the first pathology-specific generative foundation model optimized for scalable and generalizable image synthesis. Leveraging our curated dataset CPIA comprising over 148 million multi-scale, multi-organ whole-slide image patches, we pre-train a transformer-based diffusion model with pathology-aware design. To enhance both fidelity and generalization, we propose a principled capacity optimization strategy that aligns model complexity with data scale. Extensive evaluations demonstrate that OptiPathD achieves state-of-the-art performance in conditional image generation, outperforming present generative models across fidelity, diversity, and transferability metrics. Further experiments using downstream classification task on ROSE dataset confirm the efficacy of our generated images. Our work provides a foundation for generative pathology modeling, offering a scalable, domain-specialized, and transferable solution to support data-driven clinical research and diagnostic applications.

Continuous monitoring of blood pressure (BP) waveform is challenging in clinical applications due to the invasive nature of traditional techniques. As a result, there is a growing focus on the estimation of continuous BP waveforms from photoplethysmography (PPG) signals obtained through affordable wearable Internet of Medical Things (IoMT) devices. To address this demand, we introduce diffusion for continuous noninvasive BP (DiffCNBP), a lightweight model that employs a joint optimization approach incorporating sequence learning, diffusion modeling, and conditional embedding. The sequence learning module comprises stacked Transformer encoders to capture the temporal information of the PPG signals. These dynamically related features are then fed into a lightweight diffusion module based on structured state-space sequence encoders to learn detailed variation features associated with vascular dynamics. Additionally, the conditional embedding module introduces constraints to incorporate physiologically specific prior information for model estimation, thereby enhancing the fidelity of the estimated BP waveform. Our proposed method was validated using subject-wise fivefold cross-validation on a multisource database of 1415 subjects. This database included data from intensive care unit IoMT applications, where finger-based PPG sensors were employed alongside invasive BP sensors. Experimental results demonstrate that DiffCNBP outperforms other state-of-the-art methods with an average root-mean-square error of 4.36 mmHg for waveform estimation. The mean error ± standard deviation error of systolic and diastolic BP was$0.67~\pm ~4.29$mmHg and$0.37~\pm ~2.46$mmHg, respectively, meeting the clinical standards. Furthermore, we demonstrated the robust long-term trend-tracking ability of DiffCNBP on resource-constrained devices, indicating its potential IoMT deployment in clinical settings.

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

Ambulatory blood pressure (BP) monitoring plays a critical role in the early prevention and diagnosis of cardiovascular diseases. However, cuff-based inflatable devices cannot be used for continuous BP monitoring, while pulse transit time or multi-parameter-based methods require more bioelectrodes to acquire electrocardiogram signals. Thus, estimating the BP waveforms only based on photoplethysmography (PPG) signals for continuous BP monitoring has essential clinical values. Nevertheless, extracting useful features from raw PPG signals for fine-grained BP waveform estimation is challenging due to the physiological variation and noise interference. For single PPG analysis utilizing deep learning methods, the previous works depend mainly on stacked convolution operation, which ignores the underlying complementary time-dependent information. Thus, this work presents a novel Transformer-based method with knowledge distillation (KD-Informer) for BP waveform estimation. Meanwhile, we integrate the prior information of PPG patterns, selected by a novel backward elimination algorithm, into the knowledge transfer branch of the KD-Informer. With these strategies, the model can effectively capture the discriminative features through a lightweight architecture during the learning process. Then, we further adopt an effective transfer learning technique to demonstrate the excellent generalization capability of the proposed model using two independent multicenter datasets. Specifically, we first fine-tuned the KD-Informer with a large and high-quality dataset (Mindray dataset) and then transferred the pre-trained model to the target domain (MIMIC dataset). The experimental test results on the MIMIC dataset showed that the KD-Informer exhibited an estimation error of 0.02 ± 5.93 mmHg for systolic BP (SBP) and 0.01 ± 3.87 mmHg for diastolic BP (DBP), which complied with the association for the advancement of medical instrumentation (AAMI) standard. These results demonstrate that the KD-Informer has high reliability and elegant robustness to measure continuous BP waveforms.

Pancreatic cancer is one of the most malignant cancers with high mortality. The rapid on-site evaluation (ROSE) technique can significantly accelerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. However, the broader expansion of ROSE diagnosis has been hindered by the shortage of experienced pathologists. Deep learning has great potential for the automatic classification of ROSE images in diagnosis. But it is challenging to model the complicated local and global image features. The traditional convolutional neural network (CNN) structure can effectively extract spatial features, while it tends to ignore global features when the prominent local features are misleading. In contrast, the Transformer structure has excellent advantages in capturing global features and long-range relations, while it has limited ability in utilizing local features. We propose a multi-stage hybrid Transformer (MSHT) to combine the strengths of both, where a CNN backbone robustly extracts multi-stage local features at different scales as the attention guidance, and a Transformer encodes them for sophisticated global modeling. Going beyond the strength of each single method, the MSHT can simultaneously enhance the Transformer global modeling ability with the local guidance from CNN features. To evaluate the method in this unexplored field, a dataset of 4240 ROSE images is collected where MSHT achieves 95.68% in classification accuracy with more accurate attention regions. The distinctively superior results compared to the state-of-the-art models make MSHT extremely promising for cytopathological image analysis.

The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic.

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above, we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting a lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at https://github.com/hsiangyuzhao/PANet.