arXiv:2511.01698v1 Announce Type: new 
Abstract: Compared to hematoxylin-eosin (H&E) staining, immunohistochemistry (IHC) not only maintains the structural features of tissue samples, but also provides high-resolution protein localization, which is essential for aiding in pathology diagnosis. Despite its diagnostic value, IHC remains a costly and labor-intensive technique. Its limited scalability and constraints in multiplexing further hinder widespread adoption, especially in resource-limited settings. Consequently, researchers are increasingly exploring computational stain translation techniques to synthesize IHC-equivalent images from H&E-stained slides, aiming to extract protein-level information more efficiently and cost-effectively. However, most existing stain translation techniques rely on a linearly weighted summation of multiple loss terms within a single objective function, strategy that often overlooks the interdepedence among these components-resulting in suboptimal image quality and an inability to simultaneously preserve structural authenticity and color fidelity. To address this limitation, we propose a novel network architecture that follows a progressive structure, incorporating color and cell border generation logic, which enables each visual aspect to be optimized in a stage-wise and decoupled manner. To validate the effectiveness of our proposed network architecture, we build upon the Adaptive Supervised PatchNCE (ASP) framework as our baseline. We introduce additional loss functions based on 3,3'-diaminobenzidine (DAB) chromogen concentration and image gradient, enhancing color fidelity and cell boundary clarity in the generated IHC images. By reconstructing the generation pipeline using our structure-color-cell boundary progressive mechanism, experiments on HER2 and ER datasets demonstrated that the model significantly improved visual quality and achieved finer structural details.

تسلط دراسة حديثة الضوء على مزايا الكيمياء المناعية (IHC) مقارنة بصبغة الهيماتوكسيليين-إيوزين (H&E) التقليدية، خاصة في الحفاظ على بنية الأنسجة وتوفير تحديد دقيق للبروتينات، وهو أمر حاسم للتشخيصات في علم الأمراض. ومع ذلك، فإن التكاليف العالية وكثافة العمل في IHC تمثل تحديات أمام استخدامها على نطاق أوسع، خاصة في البيئات ذات الموارد المحدودة. هذه الأبحاث مهمة لأنها تبرز الحاجة إلى تقنيات تشخيصية أكثر سهولة في علم الأمراض، مما قد يحسن النتائج للمرضى.

Un estudio reciente destaca las ventajas de la inmunohistoquímica (IHC) sobre la tinción tradicional de hematoxilina-eosina (H&E), especialmente en el mantenimiento de la estructura del tejido y en la localización detallada de proteínas, crucial para los diagnósticos en patología. Sin embargo, los altos costos y la intensidad laboral de la IHC representan desafíos para su uso más amplio, especialmente en entornos con recursos limitados. Esta investigación es significativa ya que subraya la necesidad de técnicas de diagnóstico más accesibles en patología, lo que podría mejorar los resultados para los pacientes.

Une étude récente met en avant les avantages de l'immunohistochimie (IHC) par rapport à la coloration traditionnelle à l'hématoxyline-éosine (H&E), notamment en maintenant la structure des tissus et en fournissant une localisation précise des protéines essentielle pour les diagnostics pathologiques. Cependant, les coûts élevés et l'intensité de travail de l'IHC posent des défis à son utilisation plus large, en particulier dans les milieux à ressources limitées. Cette recherche est significative car elle souligne la nécessité de techniques de diagnostic plus accessibles en pathologie, ce qui pourrait améliorer les résultats pour les patients.

A recent study highlights the advantages of immunohistochemistry (IHC) over traditional hematoxylin-eosin (H&E) staining, particularly in maintaining tissue structure and providing detailed protein localization crucial for pathology diagnoses. However, the high costs and labor intensity of IHC pose challenges for its broader use, especially in resource-limited settings. This research is significant as it underscores the need for more accessible diagnostic techniques in pathology, which could improve patient outcomes.

Progressive Translation of H&E to IHC with Enhanced Structural Fidelity

arXiv:2511.18946v1 Announce Type: new 
Abstract: In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just in 2024. Most studies use publicly available datasets of H&E-IHC pairs from consecutive tissue sections. Recognizing the training challenges, many authors develop complex virtual staining models based on conditional Generative Adversarial Networks, but ignore the impact of adversarial loss on the quality of virtual staining. Furthermore, overlooking the issues of model evaluation, they claim improved performance based on metrics such as SSIM and PSNR, which are not sufficiently robust to evaluate the quality of virtually stained images. In this paper, we developed CSSP2P GAN, which we demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation. Furthermore, while iteratively developing our model, we study the impact of the adversarial loss and demonstrate its crucial role in the quality of virtually stained images. Finally, while comparing our model with reference works in the field, we underscore the limitations of the currently used evaluation metrics and demonstrate the superior performance of CSSP2P GAN.

دراسة حديثة تستكشف استخدام التعلم العدائي لتعزيز دقة تقنيات التلوين الافتراضي في علم الأمراض، خاصة في سياق تحويل صبغة H&E إلى المناعية الكيميائية (IHC). تهدف هذه الطريقة إلى معالجة القيود المفروضة على أساليب التلوين التقليدية، التي غالبًا ما تكون مكلفة وتستغرق وقتًا طويلاً. تبرز الأبحاث أهمية تقييم تأثير خسارة الخصم على جودة الصور الملونة افتراضيًا، وهو عامل غالبًا ما يتم تجاهله في الدراسات الحالية.

Un estudio reciente explora el uso del aprendizaje adversarial para mejorar la fidelidad de las técnicas de tinción virtual en patología, particularmente en el contexto de la traducción de la tinción H&E a la inmunohistoquímica (IHC). Este enfoque busca abordar las limitaciones de los métodos de tinción tradicionales, que a menudo son costosos y laboriosos. La investigación destaca la importancia de evaluar el impacto de la pérdida adversarial en la calidad de las imágenes teñidas virtualmente, un factor a menudo pasado por alto en estudios existentes.

Une étude récente examine l'utilisation de l'apprentissage adversarial pour améliorer la fidélité des techniques de coloration virtuelle en pathologie, en particulier dans le contexte de la traduction de la coloration H&E vers l'immunohistochimie (IHC). Cette approche vise à répondre aux limites des méthodes de coloration traditionnelles, souvent coûteuses et laborieuses. La recherche souligne l'importance d'évaluer l'impact de la perte adversariale sur la qualité des images colorées virtuellement, un facteur souvent négligé dans les études existantes.

A recent study explores the use of adversarial learning to enhance the fidelity of virtual staining techniques in pathology, particularly in the context of translating H&E staining to immunohistochemistry (IHC). This approach aims to address the limitations of traditional staining methods, which are often costly and labor-intensive. The research highlights the importance of evaluating the impact of adversarial loss on the quality of virtually stained images, a factor often overlooked in existing studies.

Progressive Translation of H&E to IHC with Enhanced Structural Fidelity

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