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تسجيل مستخدم جديدالتنافسية الحالية المستنيرة لصنع الأوعية الرئوية في صور CT
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
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إن حجم البيانات الكبير هو ضروري لتدريب أنظمة التعلم العميق، لكن يكلف التزامن مع جمع البيانات الطبية الصورية. يمكن أن يكون سائلاً تحسين مهام التعلم المشروع باستخدام تركيب الكائنات المهمة مثل الجزيئات الرئوية في الصور الطبية بناءً على توزيع البيانات المؤشرة، وذلك خصوصاً عندما تكون البيانات محدودة بالحجم والتوازن الفئة. في هذا البحث، نقترح الإطار التحايلي الحاسوبي المتعلق بالفئة لتحسين الجزيئات الرئوية في صور CT. يتم بناء الإطار مع الرسم الشفاف الشائع (المولد) ومختلف المحاكاة المتعلقة بالفئة. بالتوجيه على المتغيرات العشوائية الخفية وتسميات الجزيئات المستهدفة، يمكن للشبكات المدربة إنتاج جزيئات متنوعة بنفس السياق. بالتقييم على مجموعة بيانات LIDC-IDRI العامة، نظرنا في تطبيق عينة للإطار المقترح لتحسين دقة تقدير الخطر الخطير للجزيئات الرئوية كمشكلة تصنيف ثنائي، والتي تعتبر مهمة في سيناريو فحص الرئة. نظرنا أن تجميع الشرائح الصور الحقيقية والجزيئات الرئوية المصنوعة في مجموعة التدريب يمكن أن يحسن نتيجة التصنيف AUC المتوسطة عبر مختلف أنظمة الشبكات بنسبة 2٪.
Though large-scale datasets are essential for training deep learning systems, it is expensive to scale up the collection of medical imaging datasets. Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance. In this paper, we propose the class-aware adversarial synthesis framework to synthesize lung nodules in CT images. The framework is built with a coarse-to-fine patch in-painter (generator) and two class-aware discriminators. By conditioning on the random latent variables and the target nodule labels, the trained networks are able to generate diverse nodules given the same context. By evaluating on the public LIDC-IDRI dataset, we demonstrate an example application of the proposed framework for improving the accuracy of the lung nodule malignancy estimation as a binary classification problem, which is important in the lung screening scenario. We show that combining the real image patches and the synthetic lung nodules in the training set can improve the mean AUC classification score across different network architectures by 2%.
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