Improving CNN Training using Disentanglement for Liver Lesion Classification in CT

Avi Ben-Cohen, Roey Mechrez, Noa Yedidia, Hayit Greenspan

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

ملخص

Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data which are separated using a disentanglement based scheme. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيف2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
ناشرInstitute of Electrical and Electronics Engineers Inc.
الصفحات886-889
عدد الصفحات4
رقم المعيار الدولي للكتب (الإلكتروني)9781538613115
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - يوليو 2019
الحدث41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, ألمانيا
المدة: ٢٣ يوليو ٢٠١٩٢٧ يوليو ٢٠١٩

سلسلة المنشورات

الاسمProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

!!Conference

!!Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
الدولة/الإقليمألمانيا
المدينةBerlin
المدة٢٣/٠٧/١٩٢٧/٠٧/١٩

All Science Journal Classification (ASJC) codes

  • !!Signal Processing
  • !!Biomedical Engineering
  • !!Computer Vision and Pattern Recognition
  • !!Health Informatics

بصمة

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