arXiv:2510.20933v1 Announce Type: new 
Abstract: Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monitoring because it provides precise morphological and spatial information on anatomical structures that directly influence treatment decisions. Convolutional neural networks significantly impact image segmentation; however, since convolution operations are local, capturing global contextual information and long-range dependencies is still challenging. Their capacity to precisely segment structures with complicated borders and a variety of sizes is impacted by this restriction. Since transformers use self-attention methods to capture global context and long-range dependencies efficiently, integrating transformer-based architecture with CNNs is a feasible approach to overcoming these challenges. To address these challenges, we propose the Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation, referred to as FM-BFF-Net in the remainder of this paper. The network combines convolutional and transformer components, employs a focal modulation attention mechanism to refine context awareness, and introduces a bidirectional feature fusion module that enables efficient interaction between encoder and decoder representations across scales. Through this design, FM-BFF-Net enhances boundary precision and robustness to variations in lesion size, shape, and contrast. Extensive experiments on eight publicly available datasets, including polyp detection, skin lesion segmentation, and ultrasound imaging, show that FM-BFF-Net consistently surpasses recent state-of-the-art methods in Jaccard index and Dice coefficient, confirming its effectiveness and adaptability for diverse medical imaging scenarios.

تقدم دراسة جديدة شبكة تعديل بؤري ودمج ميزات ثنائية الاتجاه تهدف إلى تحسين تقسيم الصور الطبية. هذه الخطوة مهمة لأن التقسيم الدقيق يلعب دورًا حيويًا في البيئات السريرية، مما يؤثر على تشخيص الأمراض وتخطيط العلاج. من خلال تحسين القدرة على التقاط المعلومات السياقية المحلية والعالمية، يمكن أن يؤدي هذا النهج المبتكر إلى نتائج أفضل للمرضى ومراقبة أكثر فعالية لتطور الأمراض.

Un nuevo estudio presenta una Red de Modulación Focal y Fusión de Características Bidireccional destinada a mejorar la segmentación de imágenes médicas. Este avance es crucial, ya que una segmentación precisa desempeña un papel vital en entornos clínicos, influyendo en el diagnóstico de enfermedades y la planificación del tratamiento. Al mejorar la capacidad de capturar tanto información contextual local como global, este enfoque innovador podría conducir a mejores resultados para los pacientes y a un monitoreo más efectivo de la progresión de enfermedades.

Une nouvelle étude présente un réseau de modulation focale et de fusion de caractéristiques bidirectionnelles visant à améliorer la segmentation d'images médicales. Cette avancée est cruciale car une segmentation précise joue un rôle vital dans les milieux cliniques, influençant le diagnostic des maladies et la planification des traitements. En améliorant la capacité à capturer à la fois des informations contextuelles locales et globales, cette approche innovante pourrait conduire à de meilleurs résultats pour les patients et à un suivi plus efficace de la progression des maladies.

A new study introduces a Focal Modulation and Bidirectional Feature Fusion Network aimed at enhancing medical image segmentation. This advancement is crucial as accurate segmentation plays a vital role in clinical settings, influencing disease diagnosis and treatment planning. By improving the ability to capture both local and global contextual information, this innovative approach could lead to better patient outcomes and more effective monitoring of disease progression.

Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation

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