arXiv:2510.07492v3 Announce Type: replace 
Abstract: Computed Tomography (CT) is a vital diagnostic tool in clinical practice, yet the health risks associated with ionizing radiation cannot be overlooked. Low-dose CT (LDCT) helps mitigate radiation exposure but simultaneously leads to reduced image quality. Consequently, researchers have sought to reconstruct clear images from LDCT scans using artificial intelligence-based image enhancement techniques. However, these studies typically rely on synthetic LDCT images for algorithm training, which introduces significant domain-shift issues and limits the practical effectiveness of these algorithms in real-world scenarios. To address this challenge, we constructed a real-world paired lung dataset, referred to as Patient-uLDCT (ultra-low-dose CT), by performing multiple scans on volunteers. The radiation dose for the low-dose images in this dataset is only 2% of the normal dose, substantially lower than the conventional 25% low-dose and 10% ultra-low-dose levels. Furthermore, to resolve the anatomical misalignment between normal-dose and uLDCT images caused by respiratory motion during acquisition, we propose a novel purification strategy to construct corresponding aligned image pairs. Finally, we introduce a Frequency-domain Flow Matching model (FFM) that achieves excellent image reconstruction performance. Code is available at https://github.com/MonkeyDadLufy/flow-matching.

تعتبر الأشعة المقطعية (CT) أداة تشخيصية حيوية في الممارسة السريرية، لكن المخاطر الصحية المرتبطة بالإشعاع المؤين لا يمكن تجاهلها. تساعد الأشعة المقطعية منخفضة الجرعة (LDCT) في تقليل التعرض للإشعاع، لكنها تؤدي في الوقت نفسه إلى تقليل جودة الصورة. لذلك، سعى الباحثون إلى إعادة بناء صور واضحة من مسح LDCT باستخدام تقنيات تعزيز الصور المعتمدة على الذكاء الاصطناعي، ومع ذلك، تعتمد هذه الدراسات عادةً على صور LDCT الاصطناعية لتدريب الخوارزميات، مما يقدم مشكلات كبيرة في التحول بين المجالات.

La tomografía computarizada (CT) es esencial para el diagnóstico clínico, pero los riesgos para la salud asociados con la radiación ionizante no pueden pasarse por alto. La CT de baja dosis (LDCT) ayuda a mitigar la exposición a la radiación, pero al mismo tiempo reduce la calidad de la imagen. Los investigadores han desarrollado un marco basado en inteligencia artificial para reconstruir imágenes claras a partir de escaneos de LDCT utilizando un conjunto de datos de pulmón emparejado del mundo real llamado Patient-uLDCT, que presenta imágenes a dosis ultra-bajas al 2% de la dosis normal.

La tomographie par ordinateur (CT) est essentielle pour le diagnostic clinique, mais les risques pour la santé associés aux radiations ionisantes ne peuvent être ignorés. La CT à faible dose (LDCT) réduit l'exposition, mais compromet la qualité de l'image. Des chercheurs ont développé un cadre basé sur l'intelligence artificielle pour améliorer les images LDCT en utilisant un ensemble de données réelles de poumons appelé Patient-uLDCT, qui présente des images à dose ultra-faible à seulement 2 % de la dose normale de radiation.

Computed Tomography (CT) is essential for clinical diagnostics, but the associated ionizing radiation poses health risks. Low-dose CT (LDCT) reduces exposure but compromises image quality. Researchers have developed an artificial intelligence-based framework to enhance LDCT images using a real-world paired lung dataset called Patient-uLDCT, which features ultra-low-dose images at only 2% of the normal radiation dose.

A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy

arXiv:2601.08078v1 Announce Type: new 
Abstract: Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the scarcity of annotated training data. Recently, self-supervised foundation models such as DINOv3, which were trained on large natural image datasets, have shown strong potential for dense feature extraction that can help with the few-shot learning challenge. Yet, their direct application to medical images is hindered by domain differences. In this work, we propose DINO-AugSeg, a novel framework that leverages DINOv3 features to address the few-shot medical image segmentation challenge. Specifically, we introduce WT-Aug, a wavelet-based feature-level augmentation module that enriches the diversity of DINOv3-extracted features by perturbing frequency components, and CG-Fuse, a contextual information-guided fusion module that exploits cross-attention to integrate semantic-rich low-resolution features with spatially detailed high-resolution features. Extensive experiments on six public benchmarks spanning five imaging modalities, including MRI, CT, ultrasound, endoscopy, and dermoscopy, demonstrate that DINO-AugSeg consistently outperforms existing methods under limited-sample conditions. The results highlight the effectiveness of incorporating wavelet-domain augmentation and contextual fusion for robust feature representation, suggesting DINO-AugSeg as a promising direction for advancing few-shot medical image segmentation. Code and data will be made available on https://github.com/apple1986/DINO-AugSeg.

تم اقتراح إطار عمل جديد يسمى DINO-AugSeg لتحسين تقسيم الصور الطبية في سيناريوهات قليلة الأمثلة من خلال الاستفادة من ميزات ذاتية الإشراف تعتمد على DINOv3. يتناول هذا النهج تحدي ندرة بيانات التدريب المعلّمة في البيئات السريرية، باستخدام تعزيز الميزات على مستوى الموجات ودمج المعلومات السياقية لتحسين دقة التقسيم عبر مختلف طرق التصوير مثل التصوير بالرنين المغناطيسي والأشعة المقطعية.

Se ha propuesto un nuevo marco llamado DINO-AugSeg para mejorar la segmentación de imágenes médicas en escenarios de pocos ejemplos, aprovechando características auto-supervisadas basadas en DINOv3. Este enfoque aborda el desafío de la escasez de datos de entrenamiento anotados en entornos clínicos, utilizando una augmentación de características a nivel de wavelet y una fusión guiada por información contextual para mejorar la precisión de la segmentación en diversas modalidades de imagen como MRI y CT.

Un nouveau cadre nommé DINO-AugSeg a été proposé pour améliorer la segmentation d'images médicales en quelques exemples en s'appuyant sur des caractéristiques auto-supervisées basées sur DINOv3. Cette approche répond au défi du manque de données d'entraînement annotées dans les milieux cliniques, en utilisant une augmentation des caractéristiques au niveau des ondelettes et une fusion guidée par des informations contextuelles pour améliorer la précision de la segmentation à travers diverses modalités d'imagerie telles que l'IRM et le CT.

A novel framework named DINO-AugSeg has been proposed to enhance few-shot medical image segmentation by leveraging DINOv3-based self-supervised features. This approach addresses the challenge of limited annotated training data in clinical settings, utilizing wavelet-based feature-level augmentation and contextual information-guided fusion to improve segmentation accuracy across various imaging modalities such as MRI and CT.

Exploiting DINOv3-Based Self-Supervised Features for Robust Few-Shot Medical Image Segmentation

arXiv:2601.08334v1 Announce Type: new 
Abstract: Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. This study evaluates the performance, efficiency, and accessibility of general-purpose and radiomics-specific AutoML frameworks on diverse radiomics classification tasks, thereby highlighting development needs for radiomics. Ten public/private radiomics datasets with varied imaging modalities (CT/MRI), sizes, anatomies and endpoints were used. Six general-purpose and five radiomics-specific frameworks were tested with predefined parameters using standardized cross-validation. Evaluation metrics included AUC, runtime, together with qualitative aspects related to software status, accessibility, and interpretability. Simplatab, a radiomics-specific tool with a no-code interface, achieved the highest average test AUC (81.81%) with a moderate runtime (~1 hour). LightAutoML, a general-purpose framework, showed the fastest execution with competitive performance (78.74% mean AUC in six minutes). Most radiomics-specific frameworks were excluded from the performance analysis due to obsolescence, extensive programming requirements, or computational inefficiency. Conversely, general-purpose frameworks demonstrated higher accessibility and ease of implementation. Simplatab provides an effective balance of performance, efficiency, and accessibility for radiomics classification problems. However, significant gaps remain, including the lack of accessible survival analysis support and the limited integration of feature reproducibility and harmonization within current AutoML frameworks. Future research should focus on adapting AutoML solutions to better address these radiomics-specific challenges.

أظهرت دراسة حديثة تقييمًا لأداء وكفاءة وإمكانية الوصول إلى أطر التعلم الآلي الآلي (AutoML) في مجال الراديوميات، مع التركيز على قدرتها على مساعدة الباحثين الذين لا يمتلكون مهارات برمجية في تطوير نماذج تنبؤية. اختبرت الدراسة ستة أطر عامة وخمسة أطر محددة للراديوميات عبر عشرة مجموعات بيانات متنوعة، مما يكشف عن الحاجة إلى مزيد من التطوير المخصص لتحديات الراديوميات.

Un estudio reciente evaluó el rendimiento, la eficiencia y la accesibilidad de los marcos de aprendizaje automático automatizado (AutoML) en el campo de la radiómica, centrándose en su capacidad para ayudar a investigadores sin habilidades de programación a desarrollar modelos predictivos. El estudio probó seis marcos de propósito general y cinco marcos específicos de radiómica en diez conjuntos de datos diversos, revelando la necesidad de un mayor desarrollo adaptado a los desafíos de la radiómica.

Une étude récente a évalué la performance, l'efficacité et l'accessibilité des cadres d'apprentissage automatique automatisé (AutoML) dans le domaine de la radiomique, en se concentrant sur leur capacité à aider les chercheurs sans compétences en programmation à développer des modèles prédictifs. L'étude a testé six cadres à usage général et cinq cadres spécifiques à la radiomique sur dix ensembles de données divers, révélant le besoin de développements supplémentaires adaptés aux défis de la radiomique.

A recent study evaluated the performance, efficiency, and accessibility of automated machine learning (AutoML) frameworks in the field of radiomics, focusing on their ability to assist researchers without programming skills in developing predictive models. The study tested six general-purpose and five radiomics-specific frameworks across ten diverse datasets, revealing the need for further development tailored to radiomics challenges.

A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy

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