Tıbbi Görüntü İşlemede Derin Öğrenme: Teori, Mimariler ve Uygulamalar
Özet
Bu bölüm, tıbbi görüntüleme (MR, BT, X-ışını) verilerinin teşhis ve tedavi planlamasındaki kritik rolünü ve bu büyük veri hacmini yorumlamada Derin Öğrenme (DL) yöntemlerinin gerekliliğini incelemektedir. DL'nin, geleneksel Makine Öğreniminden farklı olarak, öznitelik çıkarma ve sınıflandırmayı tek bir süreçte birleştirme yeteneği vurgulanmaktadır. Bölüm, DL'nin temel yapı taşları olan Evrişim, Havuzlama ve Aktivasyon katmanları ile başlıca mimarilerini (özellikle tıbbi segmentasyonda kullanılan U-Net gibi) detaylandırır. Ayrıca, tıbbi görüntülemede veri yetersizliği sorununu çözmek için Transfer Öğrenme stratejisinin önemi ve büyük ölçekli önceden eğitilmiş modellerin (AlexNet, ResNet) bu alana nasıl adapte edileceği de açıklanmıştır. Genel olarak bölüm, tıbbi görüntüleme analizinde DL'nin teorik temelini, mimarilerini ve uygulama yaklaşımlarını kapsamlı bir şekilde sunmaktadır.
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