arXiv:2510.23224v1 Announce Type: new 
Abstract: The rapid digitization of histopathology slides has opened up new possibilities for computational tools in clinical and research workflows. Among these, content-based slide retrieval stands out, enabling pathologists to identify morphologically and semantically similar cases, thereby supporting precise diagnoses, enhancing consistency across observers, and assisting example-based education. However, effective retrieval of whole slide images (WSIs) remains challenging due to their gigapixel scale and the difficulty of capturing subtle semantic differences amid abundant irrelevant content. To overcome these challenges, we present PathSearch, a retrieval framework that unifies fine-grained attentive mosaic representations with global-wise slide embeddings aligned through vision-language contrastive learning. Trained on a corpus of 6,926 slide-report pairs, PathSearch captures both fine-grained morphological cues and high-level semantic patterns to enable accurate and flexible retrieval. The framework supports two key functionalities: (1) mosaic-based image-to-image retrieval, ensuring accurate and efficient slide research; and (2) multi-modal retrieval, where text queries can directly retrieve relevant slides. PathSearch was rigorously evaluated on four public pathology datasets and three in-house cohorts, covering tasks including anatomical site retrieval, tumor subtyping, tumor vs. non-tumor discrimination, and grading across diverse organs such as breast, lung, kidney, liver, and stomach. External results show that PathSearch outperforms traditional image-to-image retrieval frameworks. A multi-center reader study further demonstrates that PathSearch improves diagnostic accuracy, boosts confidence, and enhances inter-observer agreement among pathologists in real clinical scenarios. These results establish PathSearch as a scalable and generalizable retrieval solution for digital pathology.

تعد التطورات الأخيرة في استرجاع علم الأمراض متعدد الوسائط من خلال المحاذاة الانتباهية بين الرؤية واللغة بمثابة ثورة في مجال علم الأمراض النسيجي. تتيح هذه الطريقة المبتكرة للأطباء الشرعيين استرجاع ومقارنة الحالات المماثلة بكفاءة، مما يساعد ليس فقط في إجراء تشخيصات دقيقة، ولكن أيضًا يعزز التناسق بين المراقبين المختلفين. مع استمرار رقمنة شرائح علم الأمراض النسيجي، ستعمل هذه الأدوات على تحسين الممارسات التعليمية ونتائج المرضى، مما يجعل هذا التطور مهمًا للتطبيقات السريرية والبحثية على حد سواء.

Los recientes avances en la recuperación multimodal de patología a través de la alineación atenta entre visión y lenguaje están a punto de revolucionar el campo de la histopatología. Este enfoque innovador permite a los patólogos recuperar y comparar casos similares de manera eficiente, lo que no solo ayuda a realizar diagnósticos precisos, sino que también promueve la coherencia entre diferentes observadores. A medida que la digitalización de las diapositivas de histopatología sigue creciendo, estas herramientas mejorarán las prácticas educativas y los resultados para los pacientes, lo que hace que este desarrollo sea significativo tanto para aplicaciones clínicas como de investigación.

Les récentes avancées dans la récupération multimodale en pathologie grâce à l'alignement attentif entre vision et langage sont prêtes à révolutionner le domaine de l'histopathologie. Cette approche innovante permet aux pathologistes de récupérer et de comparer efficacement des cas similaires, ce qui aide non seulement à établir des diagnostics précis, mais favorise également la cohérence entre différents observateurs. Alors que la numérisation des lames d'histopathologie continue de croître, ces outils amélioreront les pratiques éducatives et les résultats pour les patients, rendant ce développement significatif tant pour les applications cliniques que pour la recherche.

The recent advancements in multimodal pathology retrieval through attentive vision-language alignment are set to revolutionize the field of histopathology. This innovative approach allows pathologists to efficiently retrieve and compare similar cases, which not only aids in making accurate diagnoses but also promotes consistency among different observers. As the digitization of histopathology slides continues to grow, these tools will enhance educational practices and improve patient outcomes, making this development significant for both clinical and research applications.

Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment

arXiv:2601.08127v1 Announce Type: new 
Abstract: The development of robust artificial intelligence models for histopathology diagnosis is severely constrained by the scarcity of expert-annotated lesion data, particularly for rare pathologies and underrepresented disease subtypes. While data augmentation offers a potential solution, existing methods fail to generate sufficiently realistic lesion morphologies that preserve the complex spatial relationships and cellular architectures characteristic of histopathological tissues. Here we present PathoGen, a diffusion-based generative model that enables controllable, high-fidelity inpainting of lesions into benign histopathology images. Unlike conventional augmentation techniques, PathoGen leverages the iterative refinement process of diffusion models to synthesize lesions with natural tissue boundaries, preserved cellular structures, and authentic staining characteristics. We validate PathoGen across four diverse datasets representing distinct diagnostic challenges: kidney, skin, breast, and prostate pathology. Quantitative assessment confirms that PathoGen outperforms state-of-the-art generative baselines, including conditional GAN and Stable Diffusion, in image fidelity and distributional similarity. Crucially, we show that augmenting training sets with PathoGen-synthesized lesions enhances downstream segmentation performance compared to traditional geometric augmentations, particularly in data-scarce regimes. Besides, by simultaneously generating realistic morphology and pixel-level ground truth, PathoGen effectively overcomes the manual annotation bottleneck. This approach offers a scalable pathway for developing generalizable medical AI systems despite limited expert-labeled data.

يمثل تقديم PathoGen، وهو نموذج توليدي يعتمد على الانتشار، تقدمًا كبيرًا في تركيب الآفات الواقعية في صور علم الأمراض النسيجية، حيث يعالج النقص الحاد في بيانات الآفات المعلّمة من قبل الخبراء، خاصةً بالنسبة للأمراض النادرة. يعزز هذا النموذج عملية إدخال الآفات في الصور الحميدة مع الحفاظ على الحدود الطبيعية للأنسجة والهياكل الخلوية.

La introducción de PathoGen, un modelo generativo basado en difusión, marca un avance significativo en la síntesis de lesiones realistas en imágenes de histopatología, abordando la escasez crítica de datos de lesiones anotados por expertos, especialmente para patologías raras. Este modelo mejora la inpainting de lesiones en imágenes benignas mientras preserva los límites naturales de los tejidos y las estructuras celulares.

L'introduction de PathoGen, un modèle génératif basé sur la diffusion, représente une avancée significative dans la synthèse de lésions réalistes dans les images d'histopathologie, répondant à la pénurie critique de données de lésions annotées par des experts, en particulier pour les pathologies rares. Ce modèle améliore l'inpainting de lésions dans des images bénignes tout en préservant les frontières naturelles des tissus et les structures cellulaires.

The introduction of PathoGen, a diffusion-based generative model, marks a significant advancement in the synthesis of realistic lesions in histopathology images, addressing the critical shortage of expert-annotated lesion data, especially for rare pathologies. This model enhances the inpainting of lesions into benign images while preserving natural tissue boundaries and cellular structures.

Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment

Was this article worth reading? Share it

LucidQuery AI

ShareSpeak

Attentive AI

The Visualizer

AIPortalX

Twofold Health

Ready to build your own newsroom?