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.

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.

Cell instance segmentation is critical to analyzing biomedical images, yet accurately distinguishing tightly touching cells remains a persistent challenge. Existing instance segmentation frameworks, including detection-based, contour-based, and distance mapping-based approaches, have made significant progress, but balancing model performance with computational efficiency remains an open problem. In this paper, we propose a novel cell instance segmentation method inspired by the four-color theorem. By conceptualizing cells as countries and tissues as oceans, we introduce a four-color encoding scheme that ensures adjacent instances receive distinct labels. This reformulation transforms instance segmentation into a constrained semantic segmentation problem with only four predicted classes, substantially simplifying the instance differentiation process. To solve the training instability caused by the non-uniqueness of four-color encoding, we design an asymptotic training strategy and encoding transformation method. Extensive experiments on various modes demonstrate our approach achieves state-of-the-art performance. The code is available at https://github.com/zhangye-zoe/FCIS.

Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significantly advanced weakly supervised WSI classification, facilitating both clinical diagnosis and localization of disease-positive regions. However, these methods often face challenges in differentiating between instances, leading to tissue misidentification and a potential degradation in classification performance. To address these limitations, we propose AttriMIL, an attribute-aware multiple instance learning framework. By dissecting the computational flow of attention-based MIL models, we introduce a multi-branch attribute scoring mechanism that quantifies the pathological attributes of individual instances. Leveraging these quantified attributes, we further establish region-wise and slide-wise attribute constraints to dynamically model instance correlations both within and across slides during training. These constraints encourage the network to capture intrinsic spatial patterns and semantic similarities between image patches, thereby enhancing its ability to distinguish subtle tissue variations and sensitivity to challenging instances. To fully exploit the two constraints, we further develop a pathology adaptive learning technique to optimize pre-trained feature extractors, enabling the model to efficiently gather task-specific features. Extensive experiments on five public datasets demonstrate that AttriMIL consistently outperforms state-of-the-art methods across various dimensions, including bag classification accuracy, generalization ability, and disease-positive region localization. The implementation code is available at https://github.com/MedCAI/AttriMIL.

Weakly supervised segmentation methods have garnered considerable attention due to their potential to alleviate the need for labor-intensive pixel-level annotations during model training. Traditional weakly supervised nuclei segmentation approaches typically involve a two-stage process: pseudo-label generation followed by network training. The performance of these methods is highly dependent on the quality of the generated pseudo-labels, which can limit their effectiveness. In this paper, we propose a novel domain-adaptive weakly supervised nuclei segmentation framework that addresses the challenge of pseudo-label generation through cross-task interaction strategies. Specifically, our approach leverages weakly annotated data to train an auxiliary detection task, which facilitates domain adaptation of the segmentation network. To improve the efficiency of domain adaptation, we introduce a consistent feature constraint module that integrates prior knowledge from the source domain. Additionally, we develop methods for pseudo-label optimization and interactive training to enhance domain transfer capabilities. We validate the effectiveness of our proposed method through extensive comparative and ablation experiments conducted on six datasets. The results demonstrate that our approach outperforms existing weakly supervised methods and achieves performance comparable to or exceeding that of fully supervised methods. Our code is available athttps://github.com/zhangye-zoe/DAWN.

Nuclei instance segmentation in histopathological images is crucial for biological analysis and cancer diagnosis. However, it faces two significant challenges: (1) poorly stained nuclei can lead to under-segmentation, as the background may be mistakenly identified as the foreground; and (2) deep textures within nuclei often result in fragmented instance predictions, as these textures can be misinterpreted as contours. To address these problems, this paper proposes a Structure Encoding and Interaction NEtwork, termed SEINE, which develops the nuclei structure modeling scheme and takes advantage of the similarity between nuclei structure to improve the integrality of instance segmentation. Specifically, SEINE introduces a contour-based structure encoding mechanism that integrates the correlation between nuclear structure and semantics, enabling a more accurate structural representation. Building on this encoding, we propose a structure-guided attention module, which uses clear nuclei as prototypes to guide the structural learning of unclear nuclei, thereby addressing the under-segmentation problem. Additionally, a position enhancement strategy applies a centroid distance constraint to reduce contour prediction errors, effectively mitigating fragmented instance segmentation. Extensive experiments demonstrate the effectiveness of SEINE, achieving state-of-the-art performance across four benchmark datasets.

Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (WSIs). However, the giga-pixeled nature of WSIs poses a great challenge for efficient MIL. Existing studies either do not consider global dependencies among instances, or use approximations such as linear attentions to model the pair-to-pair instance interactions, which inevitably brings performance bottlenecks. To tackle this challenge, we propose a framework named MamMIL for WSI analysis by cooperating the selective structured state space model (i.e., Mamba) with MIL, enabling the modeling of global instance dependencies while maintaining linear complexity. Specifically, considering the irregularity of the tissue regions in WSIs, we represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we further propose a topology-aware scanning mechanism to serialize the WSI graphs while preserving the topological relationships among the instances. Finally, in order to further perceive the topological structures among the instances and incorporate short-range feature interactions, we propose an instance aggregation block based on graph neural networks. Experiments show that MamMIL can achieve advanced performance than the state-of-the-art frameworks. The code can be accessed at https://github.com/Vison307/MamMIL.