arXiv:2510.27237v1 Announce Type: new 
Abstract: Whole slide image (WSI) analysis has emerged as an increasingly essential technique in computational pathology. Recent advances in the pathological foundation models (FMs) have demonstrated significant advantages in deriving meaningful patch-level or slide-level feature representations from WSIs. However, current pathological FMs have exhibited substantial heterogeneity caused by diverse private training datasets and different network architectures. This heterogeneity introduces performance variability when we utilize the extracted features from different FMs in the downstream tasks. To fully explore the advantage of multiple FMs effectively, in this work, we propose a novel framework for the fusion of heterogeneous pathological FMs, called FuseCPath, yielding a model with a superior ensemble performance. The main contributions of our framework can be summarized as follows: (i) To guarantee the representativeness of the training patches, we propose a multi-view clustering-based method to filter out the discriminative patches via multiple FMs' embeddings. (ii) To effectively fuse the heterogeneous patch-level FMs, we devise a cluster-level re-embedding strategy to online capture patch-level local features. (iii) To effectively fuse the heterogeneous slide-level FMs, we devise a collaborative distillation strategy to explore the connections between slide-level FMs. Extensive experiments conducted on lung cancer, bladder cancer, and colorectal cancer datasets from The Cancer Genome Atlas (TCGA) have demonstrated that the proposed FuseCPath achieves state-of-the-art performance across multiple tasks on these public datasets.

تُحدث التطورات الأخيرة في تحليل صور الشرائح الكاملة ثورة في علم الأمراض الحاسوبي. إن دمج نماذج الأساس المرضية المتنوعة يمهد الطريق للحصول على تمثيلات ميزات أكثر دقة وذات مغزى من هذه الصور. وهذا مهم لأنه يعالج التحديات التي تطرحها مجموعات البيانات التدريبية المتنوعة والهندسات المعمارية المختلفة للشبكات، مما يعزز في النهاية قدرات التشخيص ويحسن نتائج المرضى.

Los avances recientes en el análisis de imágenes de diapositivas completas están revolucionando la patología computacional. La fusión de modelos de fundación patológica heterogéneos está allanando el camino para representaciones de características más precisas y significativas a partir de estas imágenes. Esto es significativo porque aborda los desafíos planteados por los diversos conjuntos de datos de entrenamiento y las diferentes arquitecturas de red, mejorando en última instancia las capacidades de diagnóstico y los resultados para los pacientes.

Les avancées récentes dans l'analyse des images de diapositives entières révolutionnent la pathologie computationnelle. La fusion de modèles de fondation pathologique hétérogènes ouvre la voie à des représentations de caractéristiques plus précises et significatives à partir de ces images. Cela est important car cela répond aux défis posés par la diversité des ensembles de données d'entraînement et les différentes architectures de réseau, améliorant ainsi les capacités de diagnostic et les résultats pour les patients.

Recent advancements in whole slide image analysis are revolutionizing computational pathology. The fusion of heterogeneous pathology foundation models is paving the way for more accurate and meaningful feature representations from these images. This is significant because it addresses the challenges posed by diverse training datasets and varying network architectures, ultimately enhancing diagnostic capabilities and improving patient outcomes.

Fusion of Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis

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arXiv:2511.14907v1 Announce Type: new 
Abstract: Computational pathology holds substantial promise for improving diagnosis and guiding treatment decisions. Recent pathology foundation models enable the extraction of rich patch-level representations from large-scale whole-slide images (WSIs), but current approaches for aggregating these features into slide-level predictions remain constrained by design limitations that hinder generalizability and reliability. Here, we developed nnMIL, a simple yet broadly applicable multiple-instance learning framework that connects patch-level foundation models to robust slide-level clinical inference. nnMIL introduces random sampling at both the patch and feature levels, enabling large-batch optimization, task-aware sampling strategies, and efficient and scalable training across datasets and model architectures. A lightweight aggregator performs sliding-window inference to generate ensemble slide-level predictions and supports principled uncertainty estimation. Across 40,000 WSIs encompassing 35 clinical tasks and four pathology foundation models, nnMIL consistently outperformed existing MIL methods for disease diagnosis, histologic subtyping, molecular biomarker detection, and pan- cancer prognosis prediction. It further demonstrated strong cross-model generalization, reliable uncertainty quantification, and robust survival stratification in multiple external cohorts. In conclusion, nnMIL offers a practical and generalizable solution for translating pathology foundation models into clinically meaningful predictions, advancing the development and deployment of reliable AI systems in real-world settings.

إطار nnMIL يعزز علم الأمراض الحسابي من خلال ربط نماذج الأساس على مستوى اللطخات باستنتاجات سريرية على مستوى الشرائح. يستخدم أخذ عينات عشوائية على مستوى اللطخات والميزات، مما يسمح بتحسين دفعات كبيرة وتدريب فعال عبر مجموعات بيانات متنوعة. تهدف هذه الطريقة إلى تحسين موثوقية وقابلية تعميم تشخيص الأمراض وقرارات العلاج المستندة إلى صور الشرائح الكاملة.

El marco nnMIL mejora la patología computacional al vincular modelos de fundación a nivel de parches con inferencias clínicas a nivel de diapositivas. Utiliza muestreo aleatorio tanto a nivel de parches como de características, lo que permite una optimización por lotes grandes y un entrenamiento eficiente en varios conjuntos de datos. Este enfoque busca mejorar la fiabilidad y la generalizabilidad del diagnóstico de enfermedades y las decisiones de tratamiento basadas en imágenes de diapositivas completas.

Le cadre nnMIL améliore la pathologie computationnelle en reliant les modèles de fondation au niveau des patchs aux inférences cliniques au niveau des diapositives. Il utilise un échantillonnage aléatoire à la fois au niveau des patchs et des caractéristiques, permettant une optimisation par lots importants et un entraînement efficace sur divers ensembles de données. Cette approche vise à améliorer la fiabilité et la généralisabilité des diagnostics de maladies et des décisions de traitement basées sur des images de diapositives entières.

The nnMIL framework enhances computational pathology by linking patch-level foundation models to slide-level clinical inference. It utilizes random sampling at both patch and feature levels, allowing for large-batch optimization and efficient training across various datasets. This approach aims to improve the reliability and generalizability of disease diagnosis and treatment decisions based on whole-slide images.

nnMIL: A generalizable multiple instance learning framework for computational pathology

arXiv:2507.14670v2 Announce Type: replace 
Abstract: Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling, with significant implications for precision medicine and computational pathology. However, existing methods often underutilize the cross-modal representation alignment between histopathology images and gene expression profiles across multiple representational levels, thereby limiting their prediction performance. To address this, we propose Gene-DML, a unified framework that structures latent space through Dual-pathway Multi-Level discrimination to enhance correspondence between morphological and transcriptional modalities. The multi-scale instance-level discrimination pathway aligns hierarchical histopathology representations extracted at local, neighbor, and global levels with gene expression profiles, capturing scale-aware morphological-transcriptional relationships. In parallel, the cross-level instance-group discrimination pathway enforces structural consistency between individual (image/gene) instances and modality-crossed (gene/image, respectively) groups, strengthening the alignment across modalities. By jointly modeling fine-grained and structural-level discrimination, Gene-DML is able to learn robust cross-modal representations, enhancing both predictive accuracy and generalization across diverse biological contexts. Extensive experiments on public spatial transcriptomics datasets demonstrate that Gene-DML achieves state-of-the-art performance in gene expression prediction. The code and processed datasets are available at https://github.com/YXSong000/Gene-DML.

Gene-DML هو إطار مقترح لتحسين التنبؤ بتعبير الجينات من صور علم الأمراض النسيجية. من خلال استخدام نهج التمييز متعدد المستويات ذو المسارين، يعزز التوافق بين البيانات الشكلية والتعبيرية، مما قد يؤدي إلى نتائج أفضل في الطب الدقيق وعلم الأمراض الحسابي. تعالج هذه الطريقة قيود الأساليب الحالية التي لا تستغل بالكامل العلاقات عبر مستويات تمثيل مختلفة.

Gene-DML es un marco propuesto para mejorar la predicción de la expresión génica a partir de imágenes de histopatología. Al utilizar un enfoque de discriminación multi-nivel de doble vía, mejora la alineación entre los datos morfológicos y transcripcionales, lo que podría llevar a mejores resultados en medicina de precisión y patología computacional. Este método aborda las limitaciones de las técnicas existentes que no aprovechan completamente las relaciones a través de diferentes niveles de representación.

Gene-DML est un cadre proposé pour améliorer la prédiction de l'expression génique à partir d'images d'histopathologie. En utilisant une approche de discrimination multi-niveaux à double voie, il améliore l'alignement entre les données morphologiques et transcriptionnelles, ce qui pourrait conduire à de meilleurs résultats en médecine de précision et en pathologie computationnelle. Cette méthode répond aux limites des techniques existantes qui n'exploitent pas pleinement les relations à travers différents niveaux de représentation.

Gene-DML is a proposed framework designed to improve the prediction of gene expression from histopathology images. By utilizing a Dual-pathway Multi-Level discrimination approach, it enhances the alignment between morphological and transcriptional data, potentially leading to better outcomes in precision medicine and computational pathology. This method addresses the limitations of existing techniques that fail to fully exploit the relationships across different representational levels.

Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images

arXiv:2508.01293v2 Announce Type: replace 
Abstract: Multiple Instance Learning (MIL) is the leading approach for whole slide image (WSI) classification, enabling efficient analysis of gigapixel pathology slides. Recent work has introduced vision-language models (VLMs) into MIL pipelines to incorporate medical knowledge through text-based class descriptions rather than simple class names. However, when these methods rely on large language models (LLMs) to generate clinical descriptions or use fixed-length prompts to represent complex pathology concepts, the limited token capacity of VLMs often constrains the expressiveness and richness of the encoded class information. Additionally, descriptions generated solely by LLMs may lack domain grounding and fine-grained medical specificity, leading to suboptimal alignment with visual features. To address these challenges, we propose a vision-language MIL framework with two key contributions: (1) A grounded multi-agent description generation system that leverages curated pathology textbooks and agent specialization (e.g., morphology, spatial context) to produce accurate and diverse clinical descriptions; (2) A text encoding strategy using a list of descriptions rather than a single prompt, capturing fine-grained and complementary clinical signals for better alignment with visual features. Integrated into a VLM-MIL pipeline, our approach shows improved performance over single-prompt class baselines and achieves results comparable to state-of-the-art models, as demonstrated on renal and lung cancer datasets.

يقدم المقال إطارًا جديدًا يسمى GMAT، والذي يعزز التعلم متعدد الحالات (MIL) لتصنيف صور الشرائح الكاملة (WSI). من خلال دمج نماذج الرؤية-اللغة (VLMs)، يهدف GMAT إلى تحسين توليد الأوصاف السريرية التي تكون أكثر تعبيرًا وتحديدًا طبيًا. يعالج هذا القيود الموجودة في الأساليب الحالية التي تعتمد على نماذج اللغة الكبيرة (LLMs) لتوليد الأوصاف، والتي غالبًا ما تفتقر إلى الأساس في المجال والدقة الطبية التفصيلية، مما يحسن التوافق مع الميزات البصرية.

El artículo presenta un nuevo marco llamado GMAT, que mejora el Aprendizaje por Instancias Múltiples (MIL) para la clasificación de imágenes de diapositivas completas (WSI). Al integrar modelos de visión-lenguaje (VLM), GMAT busca mejorar la generación de descripciones clínicas que sean más expresivas y específicas desde el punto de vista médico. Esto aborda las limitaciones de los métodos existentes que dependen de grandes modelos de lenguaje (LLM) para generar descripciones, que a menudo carecen de fundamentación en el dominio y de especificidad médica detallada, mejorando así la alineación …

L'article présente un nouveau cadre appelé GMAT, qui améliore l'apprentissage par instances multiples (MIL) pour la classification des images de diapositives entières (WSI). En intégrant des modèles de vision-langage (VLM), GMAT vise à améliorer la génération de descriptions cliniques plus expressives et médicalement spécifiques. Cela répond aux limites des méthodes existantes qui s'appuient sur de grands modèles de langage (LLM) pour générer des descriptions, souvent dépourvues de fondement dans le domaine et de spécificité médicale détaillée, améliorant ainsi l'alignement avec les caractéris…

The article presents a new framework called GMAT, which enhances Multiple Instance Learning (MIL) for whole slide image (WSI) classification. By integrating vision-language models (VLMs), GMAT aims to improve the generation of clinical descriptions that are more expressive and medically specific. This addresses limitations in existing methods that rely on large language models (LLMs) for generating descriptions, which often lack domain grounding and detailed medical specificity, thus improving alignment with visual features.

GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification

arXiv:2511.15406v1 Announce Type: cross 
Abstract: Reliable semantic segmentation is essential for clinical decision making, yet deep models rarely provide explicit statistical guarantees on their errors. We introduce a simple post-hoc framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions. Given any pretrained segmentation model, we define a nested family of shrunken masks obtained either by increasing the score threshold or by applying morphological erosion. A labeled calibration set is used to select a single shrink parameter via conformal prediction, ensuring that, for new images that are exchangeable with the calibration data, the proportion of false positives retained in the confidence mask stays below a user-specified tolerance with high probability. The method is model-agnostic, requires no retraining, and provides finite-sample guarantees regardless of the underlying predictor. Experiments on a polyp-segmentation benchmark demonstrate target-level empirical validity. Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences. Code at https://github.com/deel-ai-papers/conseco.

تم تقديم إطار عمل جديد للتحكم في الإيجابيات الكاذبة في تقسيم الصور، مما يعزز موثوقية التقسيم الدلالي في اتخاذ القرارات السريرية. تستخدم هذه الطريقة المستقلة عن النموذج التنبؤ المتوافق لإنشاء أقنعة ثقة تحافظ على مستوى محدد مسبقًا من الإيجابيات الكاذبة، دون الحاجة إلى إعادة التدريب. تُظهر الطريقة ضمانات عالية الاحتمالية للصور الجديدة، مما يمثل تقدمًا كبيرًا في التصوير الطبي.

Se ha introducido un nuevo marco para controlar los falsos positivos en la segmentación de imágenes, mejorando la fiabilidad de la segmentación semántica en la toma de decisiones clínicas. Este enfoque independiente del modelo utiliza la predicción conformal para crear máscaras de confianza que mantienen una tolerancia definida por el usuario para los falsos positivos, sin necesidad de reentrenamiento. El método demuestra garantías de alta probabilidad para nuevas imágenes, representando un avance significativo en la imagen médica.

Un nouveau cadre pour contrôler les faux positifs dans la segmentation d'images a été introduit, améliorant la fiabilité de la segmentation sémantique dans la prise de décision clinique. Cette approche indépendante du modèle utilise la prédiction conforme pour créer des masques de confiance qui maintiennent une tolérance définie par l'utilisateur pour les faux positifs, sans nécessiter de réentraînement. La méthode démontre des garanties de haute probabilité pour de nouvelles images, représentant une avancée significative dans l'imagerie médicale.

A new framework for controlling false positives in image segmentation has been introduced, enhancing the reliability of semantic segmentation in clinical decision-making. This model-agnostic approach utilizes conformal prediction to create confidence masks that maintain a user-defined tolerance for false positives, without requiring retraining. The method demonstrates high probability guarantees for new images, making it a significant advancement in medical imaging.

Controlling False Positives in Image Segmentation via Conformal Prediction

arXiv:2504.07503v2 Announce Type: replace 
Abstract: Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state information through polarities and process information through inter-event time intervals. However, existing event filters often ignore the latter, producing outputs that are sparser than the raw input and limiting the reconstruction of continuous irradiance dynamics. We propose the Event Density Flow Filter (EDFilter), a framework that models event generation as threshold-crossing probability fluxes arising from the stochastic diffusion of irradiance trajectories. EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver, enabling real-time processing. The Rotary Event Dataset (RED), featuring microsecond-resolution ground-truth irradiance flow under controlled illumination is also presented for event quality evaluation. Experiments demonstrate that EDFilter achieves high-fidelity, physically interpretable event denoising and motion reconstruction.

تقدم الورقة فلاتر تدفق كثافة الأحداث (EDFilter)، وهو إطار مبتكر لتصفية تدفقات الأحداث يستخدم تقدير تدفق الاحتمالات لتحسين معالجة بيانات كاميرات الأحداث. تتناول هذه الطريقة قيود الفلاتر الحالية من خلال نمذجة توليد الأحداث كتيارات احتمالية تتجاوز العتبات، مما يسمح بإعادة بناء تدفق كثافة الأحداث المستمر في الوقت الحقيقي. يهدف الإطار إلى تحسين دقة وكفاءة أنظمة الرؤية المعتمدة على الأحداث.

El artículo presenta el Filtro de Flujo de Densidad de Eventos (EDFilter), un marco novedoso para el filtrado de flujos de eventos que utiliza la estimación de flujo de probabilidad para mejorar el procesamiento de datos de cámaras de eventos. Este enfoque aborda las limitaciones de los filtros de eventos existentes al modelar la generación de eventos como flujos de probabilidad que cruzan umbrales, permitiendo la reconstrucción en tiempo real del flujo de densidad de eventos continuo. El marco busca mejorar la precisión y eficiencia de los sistemas de visión basados en eventos.

L'article présente le filtre de flux de densité d'événements (EDFilter), un cadre novateur pour le filtrage de flux d'événements qui utilise l'estimation de flux de probabilité pour améliorer le traitement des données des caméras d'événements. Cette approche répond aux limitations des filtres d'événements existants en modélisant la génération d'événements comme des flux de probabilité franchissant des seuils, permettant ainsi la reconstruction en temps réel du flux de densité d'événements continu. Le cadre vise à améliorer la précision et l'efficacité des systèmes de vision basés sur des événe…

The paper introduces the Event Density Flow Filter (EDFilter), a novel framework for event stream filtering that utilizes probability flux estimation to enhance the processing of event camera data. This approach addresses the limitations of existing event filters by modeling event generation as threshold-crossing probability fluxes, allowing for real-time reconstruction of continuous event density flow. The framework aims to improve the accuracy and efficiency of event-based vision systems.

Event Stream Filtering via Probability Flux Estimation

arXiv:2511.15499v1 Announce Type: new 
Abstract: Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (EAR), a novel generation paradigm that emulates the human visual system's center-outward perception pattern. EAR unfolds image tokens in a spiral order from the center and progressively expands outward, preserving spatial continuity and enabling efficient parallel decoding. To further enhance flexibility and speed, we propose a length-adaptive decoding strategy that dynamically adjusts the number of tokens predicted at each step. This biologically inspired design not only reduces computational cost but also improves generation quality by aligning the generation order with perceptual relevance. Extensive experiments on ImageNet demonstrate that EAR achieves state-of-the-art trade-offs between fidelity and efficiency on single-scale autoregressive models, setting a new direction for scalable and cognitively aligned autoregressive image generation.

تقدم الورقة مفهوم تمثيل التوليد التلقائي الموسع (EAR)، وهو نموذج جديد لتوليد الصور يحاكي نظام الرؤية البشري من خلال إدراكه من المركز إلى الخارج. تعمل هذه الطريقة على تحسين الكفاءة من خلال نشر رموز الصورة بترتيب حلزوني، مما يسمح بفك تشفير متوازي ويحافظ على الاستمرارية المكانية. بالإضافة إلى ذلك، يتم اقتراح استراتيجية فك تشفير قابلة للتكيف مع الطول لتعزيز المرونة والسرعة، مما يقلل من التكاليف الحسابية ويحسن جودة التوليد.

El artículo presenta la Representación Autoregresiva Expandida (EAR), un nuevo paradigma para la generación visual que imita el sistema visual humano al percibir de manera centrípeta. Este método mejora la eficiencia al desplegar tokens de imagen en un orden espiral, permitiendo la decodificación paralela y preservando la continuidad espacial. Además, se propone una estrategia de decodificación adaptativa en longitud para mejorar la flexibilidad y velocidad, reduciendo así los costos computacionales y mejorando la calidad de generación.

Cet article présente la Représentation Autoregressive Élargie (EAR), un nouveau paradigme pour la génération visuelle qui imite le système visuel humain en percevant de manière centripète. Cette méthode améliore l'efficacité en déroulant les tokens d'image dans un ordre spiralé, permettant un décodage parallèle et préservant la continuité spatiale. De plus, une stratégie de décodage adaptative en longueur est proposée pour améliorer la flexibilité et la rapidité, réduisant ainsi les coûts computationnels et améliorant la qualité de génération.

The paper introduces Expanding Autoregressive Representation (EAR), a new paradigm for visual generation that mimics the human visual system's center-outward perception. This method improves efficiency by unfolding image tokens in a spiral order, allowing for parallel decoding and preserving spatial continuity. Additionally, a length-adaptive decoding strategy is proposed to enhance flexibility and speed, ultimately reducing computational costs and improving generation quality.

Fusion of Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis

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