Immunohistochemistry (IHC) examination is essential for characterizing tumor subtypes, providing prognostic information, and developing personalized treatment plans. However, IHC staining preparation is more complex and expensive compared to Hematoxylin and Eosin (H&E) staining, limiting its widespread clinical application. Transforming H&E images into IHC images presents a promising solution. In this paper, we propose OT-StainNet, a novel virtual IHC staining method. OT-StainNet employs a pre-trained diffusion model with richer prior knowledge as the generator and fine-tunes it with LoRA adapters through adversarial training. Given that adjacent images of the same tissue stained with H&E and IHC are not precisely aligned at the pixel level, existing methods struggle to fully utilize the supervisory information from weakly paired IHC images. To address this issue, we propose an optimal transport-driven semantic matching (OTSM) mechanism, establishing accurate semantic correspondences between H&E-IHC image pairs. By leveraging the real IHC features obtained through the OTSM mechanism, we design a semantic consistency constraint (SCC) to ensure that the correlations among virtual IHC features remain consistent with those among real IHC features, thereby preserving valuable correlation information during stain transfer. We validate OT-StainNet using four types of IHC staining across two datasets. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art approaches.

Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require splitting large images into smaller patches for training, leading to two significant issues. First, nuclei at the borders of adjacent patches often misalign during inference. Second, this patch-based approach significantly increases the model's training and inference time. Recently, Mamba has garnered attention for its ability to model large-scale images with linear time complexity and low memory consumption. It offers a promising solution for training nuclei segmentation and classification models on full-sized images. However, the Mamba orientation-based scanning method lacks account for category-specific features, resulting in suboptimal performance in scenarios with imbalanced class distributions. To address these challenges, this paper introduces a novel scanning strategy based on category probability sorting, which independently ranks and scans features for each category according to confidence from high to low. This approach enhances the feature representation of uncertain samples and mitigates the issues caused by imbalanced distributions. Extensive experiments conducted on four public datasets demonstrate that our method outperforms state-of-the-art approaches, delivering superior performance in nuclei segmentation and classification tasks.

High-quality pathology images are essential for accurate clinical diagnosis and treatment. However, acquiring high-resolution (HR) pathology images is often hindered by equipment limitations, limited expert availability, and complex slide preparation procedures. Image super-resolution (SR), which reconstructs HR images from low-resolution (LR) inputs, offers a practical solution. However, most existing SR methods are designed for natural images and often struggle to capture the distinct structural characteristics of pathology data. In this paper, we propose Context-Aware Generative Adversarial Network(CAGAN), a novel SR framework tailored for pathological images. It introduces a context path that effectively leverages the rich spatial context in whole slide images (WSIs) while maintaining computational efficiency. In addition, considering the significant differences in staining patterns and reconstruction difficulty between the nucleus and cytoplasm, we propose a Nucleus-Enhanced Hematoxylin Channel (NEHC) loss. This loss imposes targeted constraints on nuclei to better preserve morphological consistency. Experiments on two pathological datasets demonstrate that CA-GAN achieves state-of-the-art performance in both quantitative metrics and perceptual quality. Code will be available soon.

Immunohistochemistry (IHC) examination is essential for determining breast cancer subtypes and provides critical prognostic factors to guide treatment decisions. However, the complex and expensive preparation of IHC staining limits its widespread use in clinical practice. Recent advancements in generative models have introduced virtual staining as a promising alternative, yet obtaining pixel-level paired data in clinical settings remains a significant challenge. In this paper, we propose Multi-IHC Net, which utilizes unpaired data to simultaneously generate Ki67, ER, PR, and HER2 images from H&E-stained breast tissue. Specifically, a general encoder extracts generalized features from H&E images, while interactive decoders reconstruct the four types of IHC images. Additionally, a feature alignment module also models the correlations among the different IHC stains. To enhance accuracy, we introduce a pathology consistency mechanism between H&E and adjacent IHC images. Extensive experiments demonstrate the superiority of our method compared to state-of-the-art approaches.

Panoramic segmentation of renal pathological tissues plays a crucial role in diagnosing renal carcinoma and other kidney-related diseases. The multi-scale nature of kidney tissues, which requires different magnification levels for accurate analysis, presents a significant challenge for segmentation models. In this work, we propose a Multi-scale Context Intertwining Network (MCINet) to address this issue. Our approach utilizes an auxiliary interaction network to enhance feature interaction between different scales and generate pseudo-labels for unannotated structures. By incorporating exponential moving average strategies, we ensure seamless feature integration across scales. Extensive experiments demonstrate that MCINet outperforms state-of-the-art models in key metrics such as Dice and Hausdorff Distance, proving its efficacy in renal tissue segmentation tasks.

Nuclei classification plays a pivotal role in pathological research. Recent advances in graph neural networks (GNNs) have shown great promise in modeling cell-cell interactions. However, many existing methods overlook tissue context, which is crucial for accurate nuclei identification, as nuclei exhibit distinct patterns within specific tissue structures. To address this limitation, we propose a novel Relation-Aware Graph AT-tention network (RAGAT) that effectively leverages nucleus-related features for precise classification. RAGAT constructs a cell graph based on spatial proximity and visual feature similarity, while also introducing a tissue-aware graph by sampling regions around each nucleus to capture the tissue microenvironment and depict local cellular contexts. Furthermore, RAGAT employs a hybrid graph attention module to integrate cell-cell and tissue-cell interactions, enabling a comprehensive understanding of the nuclear context. Experimental results on three benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, offering valuable insight into the analysis of nuclear microenvironments. Our code is available at https://github.com/lingboboo/RAGAT.

Immunohistochemistry (IHC) examination is essential to determine the tumour subtypes, provide key prognostic factors, and develop personalized treatment plans for breast cancer. However, compared to Hematoxylin and Eosin (H&E) staining, the preparation process of IHC staining is more complex and expensive, which limits its application in clinical practice. Therefore, H&E to IHC stain transfer may be an ideal solution to obtain IHC staining. To ensure high transferring quality, it would be much more desirable to exploit the supervised information between adjacent layer images of the same tissue, which are stained by H&E and IHC stainings, respectively. Nevertheless, adjacent layer tissue images are not accurately paired at the pixel level, which poses significant challenges to network training. To address this problem, we propose a generative adversarial network for breast IHC virtual staining, which contains an optimal transport-based supervised information mining (OT-SIM) mechanism and a pathological correlation-based supervised information mining (PC-SIM) mechanism. The OT-SIM guides the network in mining matching consistency between H&E images and the adjacent layer's real IHC images, providing as much instance-level supervision as possible. The PC-SIM further explores the consistency between the correlation among virtual IHC images and the correlation among real IHC images, providing batch-level supervision. Extensive experiments show the superiority of our method on two breast tissue benchmark datasets compared to the state-of-the-art methods both quantitatively and qualitatively. The code is available at https://github.com/xianchaoguan/SIM-GAN.

In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) images, the FS images greatly suffer from poor quality. Thus, it is of great significance to transfer the FS image to the FFPE one, which enables pathologists to observe high-quality images in operation. However, obtaining the paired FS and FFPE images is quite hard, so it is difficult to obtain accurate results using supervised methods. Apart from this, the FS to FFPE stain transfer faces many challenges. Firstly, the number and position of nuclei scattered throughout the image are hard to maintain during the transfer process. Secondly, transferring the blurry FS images to the clear FFPE ones is quite challenging. Thirdly, compared with the center regions of each patch, the edge regions are harder to transfer. To overcome these problems, a multi-perspective self-supervised GAN, incorporating three auxiliary tasks, is proposed to improve the performance of FS to FFPE stain transfer. Concretely, a nucleus consistency constraint is designed to enable the high-fidelity of nuclei, an FFPE guided image deblurring is proposed for improving the clarity, and a multi-field-of-view consistency constraint is designed to better generate the edge regions. Objective indicators and pathologists' evaluation for experiments on the five datasets across different countries have demonstrated the effectiveness of our method. In addition, the validation in the downstream task of microsatellite instability prediction has also proved the performance improvement by transferring the FS images to FFPE ones. Our code link is https://github.com/linyiyang98/Self-Supervised-FS2FFPE.git.

In histopathology, the tissue slides are usually stained by common H&E stain or special stains (MAS, PAS, and PASM, etc.) to clearly show specific tissue structures. The rapid development of deep learning provides a good solution to generate virtual staining images to significantly reduce the time and labor costs associated with histochemical staining. However, most existing methods need to train a special model for every two stains, which consumes a lot of computing resources with the increasing of staining types. To address this problem, we propose an unsupervised multi-domain stain transfer method, GramGAN, which realizes the progressive transfer through cascaded Style-Guided blocks. For each Style-Guided block, we design a style encoding dictionary to characterize and store all the staining style information. In addition, we propose a Rényi entropy-based regularization term to improve the discrimination ability of different styles. The experimental results show that our method can realize accurate transferring among multiple staining styles with better performance. Furthermore, we build and publish a special stained image dataset suitable for glomeruli segmentation (including H&E staining), where the accuracy of glomeruli detection and segmentation can be significantly improved after transferring H&E-stained images to PAS-stained and PASM-stained ones by our method. The code is publicly available at: https://github.com/xianchaoguan/GramGAN.