arXiv:2601.08776v1 Announce Type: new 
Abstract: Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescence images to H&E-like appearance can aid interpretation and integration with standard workflows. We present a Cycle-Consistent Adversarial Network (CycleGAN) approach for unpaired image-to-image translation from multi-channel fluorescence microscopy to pseudo H&E stained histopathology images. The method combines C01 and C02 fluorescence channels into RGB and learns a bidirectional mapping between fluorescence and H&E domains without paired training data. The architecture uses ResNet-based generators with residual blocks and PatchGAN discriminators, trained with adversarial, cycle-consistency, and identity losses. Experiments on fluorescence microscopy datasets show the model generates realistic pseudo H&E images that preserve morphological structures while adopting H&E-like color characteristics. This enables visualization of fluorescence data in a format familiar to pathologists and supports integration with existing H&E-based analysis pipelines.

تم تطوير نهج جديد يستخدم الشبكات التنافسية المتسقة (CycleGAN) لترجمة صور المجهر الضوئي متعدد القنوات إلى صور نسيجية مزيفة ملونة بصبغة الهيماتوكسيليين والإيوزين (H&E). تتيح هذه الطريقة الترجمة غير المتزاوجة من صورة إلى صورة، مع الحفاظ على الهياكل الشكلية مع اعتماد خصائص لونية مشابهة لتلك الخاصة بـ H&E.

Se ha desarrollado un nuevo enfoque que utiliza Redes Generativas Antagónicas Consistentes en Ciclo (CycleGAN) para traducir imágenes de microscopía de fluorescencia multicanal en imágenes histopatológicas pseudo teñidas con Hematoxilina y Eosina (H&E). Este método permite la traducción de imagen a imagen no emparejada, preservando las estructuras morfológicas mientras adopta características de color similares a las de H&E.

Une nouvelle approche utilisant des réseaux antagonistes génératifs à cycle (CycleGAN) a été développée pour traduire des images de microscopie à fluorescence multi-canaux en images histopathologiques pseudo teintées à l'hématoxyline et à l'éosine (H&E). Cette méthode permet une traduction d'image à image non appariée, préservant les structures morphologiques tout en adoptant des caractéristiques de couleur similaires à celles de H&E.

A new approach utilizing Cycle-Consistent Adversarial Networks (CycleGAN) has been developed to translate multi-channel fluorescence microscopy images into pseudo Hematoxylin and Eosin (H&E) stained histopathology images. This method allows for unpaired image-to-image translation, preserving morphological structures while adopting H&E-like color characteristics.

Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN

arXiv:2601.08499v1 Announce Type: new 
Abstract: Large models such as Vision Transformers (ViTs) have demonstrated remarkable superiority over smaller architectures like ResNet in few-shot classification, owing to their powerful representational capacity. However, fine-tuning such large models demands extensive GPU memory and prolonged training time, making them impractical for many real-world low-resource scenarios. To bridge this gap, we propose EfficientFSL, a query-only fine-tuning framework tailored specifically for few-shot classification with ViT, which achieves competitive performance while significantly reducing computational overhead. EfficientFSL fully leverages the knowledge embedded in the pre-trained model and its strong comprehension ability, achieving high classification accuracy with an extremely small number of tunable parameters. Specifically, we introduce a lightweight trainable Forward Block to synthesize task-specific queries that extract informative features from the intermediate representations of the pre-trained model in a query-only manner. We further propose a Combine Block to fuse multi-layer outputs, enhancing the depth and robustness of feature representations. Finally, a Support-Query Attention Block mitigates distribution shift by adjusting prototypes to align with the query set distribution. With minimal trainable parameters, EfficientFSL achieves state-of-the-art performance on four in-domain few-shot datasets and six cross-domain datasets, demonstrating its effectiveness in real-world applications.

يقدم EfficientFSL إطارًا للتعديل يعتمد فقط على الاستفسارات لتحسين تصنيف القليل من العينات باستخدام المحولات البصرية (ViTs)، مما يعزز الأداء مع تقليل كبير في المتطلبات الحاسوبية. تستفيد هذه الطريقة من قدرات النموذج المدرب مسبقًا، محققة دقة عالية مع عدد قليل من المعلمات القابلة للتعديل.

EfficientFSL presenta un marco de ajuste solo de consultas para Transformadores de Visión (ViTs), mejorando la clasificación de pocos disparos mientras reduce significativamente las demandas computacionales. Este enfoque aprovecha las capacidades del modelo preentrenado, logrando alta precisión con un número mínimo de parámetros.

EfficientFSL introduit un cadre de réglage uniquement basé sur les requêtes pour les Transformateurs de Vision (ViTs), améliorant la classification à faible échantillon tout en réduisant considérablement les exigences computationnelles. Cette approche exploite les capacités du modèle pré-entraîné, atteignant une haute précision avec un nombre minimal de paramètres.

EfficientFSL introduces a query-only fine-tuning framework for Vision Transformers (ViTs), enhancing few-shot classification while significantly reducing computational demands. This approach leverages the pre-trained model's capabilities, achieving high accuracy with minimal parameters.

Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN

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