arXiv:2510.21810v1 Announce Type: new 
Abstract: Diabetic Retinopathy (DR), a vision-threatening complication of Dia-betes Mellitus (DM), is a major global concern, particularly in India, which has one of the highest diabetic populations. Prolonged hyperglycemia damages reti-nal microvasculature, leading to DR symptoms like microaneurysms, hemor-rhages, and fluid leakage, which, if undetected, cause irreversible vision loss. Therefore, early screening is crucial as DR is asymptomatic in its initial stages. Fundus imaging aids precise diagnosis by detecting subtle retinal lesions. This paper introduces a hybrid diagnostic framework combining traditional feature extraction and deep learning (DL) to enhance DR detection. While handcrafted features capture key clinical markers, DL automates hierarchical pattern recog-nition, improving early diagnosis. The model synergizes interpretable clinical data with learned features, surpassing standalone DL approaches that demon-strate superior classification and reduce false negatives. This multimodal AI-driven approach enables scalable, accurate DR screening, crucial for diabetes-burdened regions.

تم تطوير إطار هجين جديد للتعلم العميق لتحسين الكشف عن اعتلال الشبكية السكري، وهو حالة خطيرة تصيب العين مرتبطة بالسكري. يجمع هذا النهج المبتكر بين الميزات التقليدية والرؤى المدفوعة بالذكاء الاصطناعي، مما يجعله ذا أهمية خاصة للدول مثل الهند، التي تواجه انتشارًا مرتفعًا للسكري. الكشف المبكر أمر حيوي حيث إن اعتلال الشبكية السكري غالبًا ما لا يظهر أعراضًا حتى فوات الأوان، مما يؤدي إلى فقدان البصر بشكل لا يمكن عكسه. يمكن أن يحسن هذا التقدم بشكل كبير من عمليات الفحص وفي النهاية ينقذ البصر للعديد من الأفراد.

Se ha desarrollado un nuevo marco híbrido de aprendizaje profundo para mejorar la detección de la retinopatía diabética, una grave afección ocular relacionada con la diabetes. Este enfoque innovador combina características tradicionales con conocimientos impulsados por IA, lo que es especialmente significativo para países como India, que enfrenta una alta prevalencia de diabetes. La detección temprana es vital, ya que la retinopatía diabética a menudo no presenta síntomas hasta que es demasiado tarde, lo que lleva a una pérdida de visión irreversible. Este avance podría mejorar enormemente los procesos de detección y, en última instancia, salvar la vista de muchas personas.

Un nouveau cadre hybride d'apprentissage profond a été développé pour améliorer la détection de la rétinopathie diabétique, une grave affection oculaire liée au diabète. Cette approche innovante combine des caractéristiques traditionnelles avec des informations pilotées par l'IA, ce qui est particulièrement significatif pour des pays comme l'Inde, qui fait face à une forte prévalence du diabète. La détection précoce est essentielle car la rétinopathie diabétique ne présente souvent aucun symptôme jusqu'à ce qu'il soit trop tard, entraînant une perte de vision irréversible. Cette avancée pourrait grandement améliorer les processus de dépistage et, en fin de compte, sauver la vue de nombreuses personnes.

A new hybrid deep learning framework has been developed to enhance the detection of diabetic retinopathy, a serious eye condition linked to diabetes. This innovative approach combines traditional features with AI-driven insights, making it particularly significant for countries like India, which faces a high prevalence of diabetes. Early detection is vital as diabetic retinopathy often shows no symptoms until it's too late, leading to irreversible vision loss. This advancement could greatly improve screening processes and ultimately save sight for many individuals.

Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights

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arXiv:2502.16697v2 Announce Type: replace-cross 
Abstract: Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not interpretable. As a result, clinicians often resort to known biomarkers for diagnosis, although biomarker-based classification typically performs worse than large neural networks. This work proposes a method that surpasses the performance of established machine learning models while simultaneously improving prediction interpretability for diabetic retinopathy staging from optical coherence tomography angiography (OCTA) images. Our method is based on a novel biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone (FAZ) in a human-interpretable way. This graph representation allows us to frame diabetic retinopathy staging as a graph-level classification task, which we solve using an efficient graph neural network. We benchmark our method against well-established baselines, including classical biomarker-based classifiers, convolutional neural networks (CNNs), and vision transformers. Our model outperforms all baselines on two datasets. Crucially, we use our biology-informed graph to provide explanations of unprecedented detail. Our approach surpasses existing methods in precisely localizing and identifying critical vessels or intercapillary areas. In addition, we give informative and human-interpretable attributions to critical characteristics. Our work contributes to the development of clinical decision-support tools in ophthalmology.

تعتبر القابلية للتفسير ضرورية لبناء الثقة في نماذج التعلم الآلي المستخدمة في التشخيصات الطبية. تقدم هذه الدراسة طريقة جديدة تعزز من قابلية تفسير مراحل اعتلال الشبكية السكري من صور OCTA، متفوقة على النماذج الحالية. تعتمد الطريقة على تمثيل رسومي هجين مستند إلى البيولوجيا لنمذجة الهياكل الشبكية، مما يسمح بفهم أوضح لعملية التصنيف.

La interpretabilidad es esencial para generar confianza en los modelos de aprendizaje automático utilizados en diagnósticos médicos. Este estudio presenta un método novedoso que mejora la interpretabilidad de la clasificación de la retinopatía diabética a partir de imágenes OCTA, superando a los modelos de aprendizaje automático existentes. La metodología utiliza una representación gráfica heterogénea informada por la biología para modelar estructuras retinianas, permitiendo una mejor comprensión del proceso de clasificación.

L'interprétabilité est essentielle pour instaurer la confiance dans les modèles d'apprentissage automatique utilisés dans le diagnostic médical. Cette étude présente une méthode novatrice qui améliore l'interprétabilité du stade de la rétinopathie diabétique à partir des images OCTA tout en surpassant les modèles d'apprentissage automatique existants. L'approche utilise une représentation graphique hétérogène informée par la biologie pour modéliser les structures rétiniennes, permettant des aperçus plus clairs du processus de classification.

Interpretability is essential for building trust in machine learning models used in medical diagnostics. This study presents a novel method that enhances the interpretability of diabetic retinopathy staging from OCTA images while outperforming existing machine learning models. The approach utilizes a biology-informed heterogeneous graph representation to model retinal structures, allowing for clearer insights into the classification process.

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

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.

Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights

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