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.

Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining

arXiv:2511.17611v1 Announce Type: new 
Abstract: Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become a cornerstone technology in clinical microbiology, enabling rapid and accurate microbial identification. However, the development of data-driven diagnostic models remains limited by the lack of sufficiently large, balanced, and standardized spectral datasets. This study investigates the use of deep generative models to synthesize realistic MALDI-TOF MS spectra, aiming to overcome data scarcity and support the development of robust machine learning tools in microbiology.
  We adapt and evaluate three generative models, Variational Autoencoders (MALDIVAEs), Generative Adversarial Networks (MALDIGANs), and Denoising Diffusion Probabilistic Model (MALDIffusion), for the conditional generation of microbial spectra guided by species labels. Generation is conditioned on species labels, and spectral fidelity and diversity are assessed using diverse metrics.
  Our experiments show that synthetic data generated by MALDIVAE, MALDIGAN, and MALDIffusion are statistically and diagnostically comparable to real measurements, enabling classifiers trained exclusively on synthetic samples to reach performance levels similar to those trained on real data. While all models faithfully reproduce the peak structure and variability of MALDI-TOF spectra, MALDIffusion obtains this fidelity at a substantially higher computational cost, and MALDIGAN shows competitive but slightly less stable behaviour. In contrast, MALDIVAE offers the most favorable balance between realism, stability, and efficiency. Furthermore, augmenting minority species with synthetic spectra markedly improves classification accuracy, effectively mitigating class imbalance and domain mismatch without compromising the authenticity of the generated data.

استكشفت دراسة حديثة استخدام نماذج توليد عميقة لتوليد طيف MALDI-TOF MS واقعي، مما يعالج القيود المفروضة من نقص مجموعات البيانات الطيفية في علم الأحياء الدقيقة السريرية. تتكيف الدراسة مع نماذج التشفير التلقائي المتغير، والشبكات التنافسية التوليدية، ونماذج الانتشار الاحتمالية لإنتاج أطياف ميكروبية مشروطة بعلامات الأنواع.

Un estudio reciente ha explorado el uso de modelos generativos profundos para sintetizar espectros MALDI-TOF MS realistas, abordando las limitaciones impuestas por conjuntos de datos espectrales insuficientes en microbiología clínica. La investigación adapta autoencoders variacionales, redes generativas antagónicas y modelos probabilísticos de difusión de denoising para generar espectros microbianos condicionados por etiquetas de especies.

Une étude récente a exploré l'utilisation de modèles génératifs profonds pour synthétiser des spectres MALDI-TOF MS réalistes, répondant aux limitations posées par des ensembles de données spectrales insuffisants en microbiologie clinique. La recherche adapte des autoencodeurs variationnels, des réseaux antagonistes génératifs et des modèles probabilistes de diffusion de débruitage pour générer des spectres microbiens conditionnés par des étiquettes d'espèces.

A recent study has explored the use of deep generative models to synthesize realistic MALDI-TOF MS spectra, addressing the limitations posed by insufficient spectral datasets in clinical microbiology. The research adapts Variational Autoencoders, Generative Adversarial Networks, and Denoising Diffusion Probabilistic Models to generate microbial spectra conditioned on species labels.

Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining

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