Deep learning based multiple sclerosis lesion detection utilizing synthetic data generation and soft attention mechanism

Omer Zucker Shmueli, Chen Solomon, Noam Ben-Eliezer, Hayit Greenspan

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


In this work we focus on identifying healthy brain slices vs brain slices with Multiple sclerosis (MS) lesions. MS is an autoimmune, demyelinating disease characterized by inflammatory lesions in the central nervous system. MRI is commonly used for diagnosis of MS, and enables accurate detection and classification of lesions for early diagnosis and treatment. Visual attention mechanisms may be beneficial for the detection of MS brain lesions, as they tend to be small. The attention mechanism prevents overfitting of the background when the amount of data is limited. In addition, enough data is necessary for training a successful machine learning algorithms for medical image analysis. Data with insufficient variability leads to poor classification performance. This is problematic in medical imaging where abnormal findings are uncommon and data labeling requires expensive expert's time. In this work, we suggest a new network architecture, based on Y-net and EfficientNet models, with attention layers to improve the network performance and reduce overfitting. Furthermore, the attention layers allow extraction of lesion locations. In addition, we show an innovative regularization scheme on the attention weight mask to make it focus on the lesions while letting it search in different areas. Finally, we explore an option to add synthetic lesions in the training process. Based on recent work, we generate artificial lesions in healthy brain MRI scans to augment our training data. Our system achieves 91% accuracy in identifying cases that contain lesions (vs. healthy cases) with more than 13% improvement over an equivalent system without the attention and the data added.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفMedical Imaging 2022
العنوان الفرعي لمنشور المضيفComputer-Aided Diagnosis
المحررونKaren Drukker, Khan M. Iftekharuddin
رقم المعيار الدولي للكتب (الإلكتروني)9781510649415
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2022
الحدثMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
المدة: ٢١ مارس ٢٠٢٢٢٧ مارس ٢٠٢٢

سلسلة المنشورات

الاسمProgress in Biomedical Optics and Imaging - Proceedings of SPIE
مستوى الصوت12033


!!ConferenceMedical Imaging 2022: Computer-Aided Diagnosis
المدينةVirtual, Online

All Science Journal Classification (ASJC) codes

  • !!Electronic, Optical and Magnetic Materials
  • !!Atomic and Molecular Physics, and Optics
  • !!Biomaterials
  • !!Radiology Nuclear Medicine and imaging


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