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.

Whole Slide Imaging (WSI) has become a gold standard in cancer diagnosis, inspecting multi-scale information from cellular to tissue levels. Processing an entire WSI directly is infeasible due to GPU memory constraints; thus, Multiple Instance Learning (MIL) has emerged as the standard solution by partitioning WSIs into tiles. While recent two-stage MIL frameworks partially achieve memory efficiency by decoupling tile-level extraction from slide-level modeling, they still face four limitations: 1) the conflict between training throughput and inference memory efficiency, 2) the high susceptibility to overfitting on small-scale WSI datasets with sparse supervision, 3) the disruption of spatial structural integrity during sampling-based training, and 4) the inadequate modeling of multi-scale feature interactions within long sequences. We therefore introduce PathRWKV, a novel State Space Model designed for efficient and robust WSI analysis. To resolve the computational trade-off, we propose an asymmetric structure utilizing max pooling aggregation, enabling parallelized training for high throughput and recurrent inference with constant ( $\mathcal {O}\text {(}{1}\text {)}$ ) memory complexity. To mitigate overfitting, we employ random sampling to enhance data diversity, with a multi-task learning module to regularize feature learning on limited data. To restore spatial context, we introduce 2D sinusoidal position encoding to perceive the relative locations of tissue tiles. To capture comprehensive representations, we integrate TimeMix and ChannelMix modules, enabling dynamic multi-scale feature modeling across temporal and spatial dimensions. Experiments on 29,073 WSIs across 11 datasets demonstrate that PathRWKV outperforms 11 state-of-the-art methods on 10 datasets, establishing it as a scalable and solution with application potential.

Spatial Transcriptomics (ST) technology detects gene expression from tissue biopsies, playing an emerging role in cancer diagnosis and precision medicine. However, the high cost of ST technology limits its broader application. Recently, deep learning approaches have provided insight into predicting gene expression based on H&E-stained histopathology images. Nevertheless, the relationship between morphological features and gene expression is highly complex. To address these challenges, we propose DiffBulk, a novel two-stage framework that leverages conditional diffusion models to learn expressive image representations enriched with gene expression information. In the first stage, we introduce a gene-to-image conditional diffusion model equipped with a permutation-invariant open-embedding gene encoder, which enables unified training across diverse gene panels. In the second stage, diffusion-derived features are fused with representations from a pathology foundation model, effectively bridging the domain gap and improving downstream gene expression prediction. We evaluate DiffBulk on high-quality Xenium ST data curated from the HEST dataset and the CrunchDAO challenge, constructing tile-level pseudo-bulk datasets for training and evaluation. Extensive experiments demonstrate that DiffBulk consistently outperforms state-of-the-art baselines across all metrics for gene expression prediction. These findings highlight the potential of diffusion-based gene-image representation learning and suggest promising directions for future research.

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.

Accurate medical image segmentation relies on collaborative modeling of local details and global semantics, especially in small-volume structures, blurred boundaries, and fine-grained anatomical regions under low signal-to-noise conditions. However, existing transformer-based methods typically suffer from feature redundancy problems caused by inaccurate attention mechanism focus and nonlinear modeling defects caused by insufficient expression ability of feedforward networks, which leads to attention bias and nonlinear modeling bias, and ultimately degrades segmentation performance. To address these challenges, we propose CGCA-KAN, a novel transformer-based framework that employs a collaborative correction mechanism (CCM) to jointly mitigate attention bias and nonlinear modeling bias. Specifically, we introduce a cluster-aware self-attention module (CASAM) to mitigate attention bias by refining semantic focus and suppressing redundancy through token group compression, thereby enhancing attention to small-volume structures. Additionally, we design a Kolmogorov-Arnold Network enhanced feedforward network (KAN-EFFN) to mitigate nonlinear modeling bias through adaptive nonlinear transformations, thereby improving the model's ability to delineate blurred boundaries. Extensive experiments on LiTS2017, Synapse, and BraTS2020 datasets demonstrate state-of-the-art performance in fine-grained segmentation of ambiguous lesions in the liver, complex structures of multiple organs, and brain tumors. Our results highlight CGCA-KAN as a promising solution for addressing modeling biases in medical image segmentation.

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.