Deep Generative Models in Medical Imaging: A Literature Review

Year 2024, EARLY VIEW, 1 - 1

Begüm Şener

Abstract

Deep learning has been used extensively in recent years in numerous studies across many disciplines, including medical imaging. GANs (Generative Adversarial Networks) have started to be widely used in the medical field due to their ability to generate realistic images. Recent research has concentrated on three different deep generative models for improving medical images, and a review of deep learning architectures for data augmentation has been done. In this article, other generative models are emphasized, given the dominance of GANs in the field. Studies have conducted a literature review comparing different deep generative models for medical image data augmentation, without focusing solely on GANs or traditional data augmentation methods. In contrast to variational autoencoders, generative adversarial networks (GANs) are the generative model that is most frequently employed for enhancing medical image data. Recent studies have shown that diffusion models have received more attention in recent years compared to variational autoencoders and GANs for medical image data augmentation. This trend is thought to be related to the fact that many GAN-related research directions have previously been investigated, making it more challenging to advance these architectures' current applications.

Keywords

Generative adversarial networks, deep learning, medical imaging, data augmentation, deep learning architecture.

Tıbbi Görüntülemede Derin Üretken Modeller : Bir Literatür Taraması

Abstract

Derin öğrenme, son yıllarda tıbbi görüntüleme de dahil olmak üzere birçok disiplinde yapılan çok sayıda çalışmada yaygın olarak kullanılmaktadır. GAN'lar (Generative Adversarial Networks), gerçekçi görüntüler üretebilme yeteneklerinden dolayı tıp alanında yaygın olarak kullanılmaya başlanmıştır. Son araştırmalar, tıbbi görüntülerin iyileştirilmesine yönelik üç farklı derin üretken modele odaklanmaktadır ve veri artırmaya yönelik derin öğrenme mimarilerinin bir incelemesi yapılmıştır. Bu makalede GAN'ların alandaki hakimiyeti dikkate alınarak diğer üretken modeller üzerinde durulmaktadır. Çalışmada, yalnızca GAN'lara veya geleneksel veri artırma yöntemlerine odaklanmadan, tıbbi görüntü verisi artırmaya yönelik farklı derin üretken modelleri karşılaştıran bir literatür taraması gerçekleştirilmiştir Değişken otomatik kodlayıcıların aksine, üretken çekişmeli ağlar (GAN'lar), tıbbi görüntü verilerini geliştirmek için en sık kullanılan üretken modeldir. Son araştırmalar, difüzyon modellerinin son yıllarda tıbbi görüntü verisi artırmaya yönelik varyasyonel otomatik kodlayıcılar ve GAN'lara kıyasla daha fazla ilgi gördüğünü göstermiştir. Bu eğilimin, GAN ile ilgili birçok araştırma yönünün daha önce araştırılmış olmasıyla ilişkili olduğu ve bu mimarilerin mevcut uygulamalarını geliştirmeyi daha da zorlaştırdığı düşünülmektedir.

Keywords

Üretici çekişmeli ağlar, derin öğrenme, tıbbi görüntüleme, veri büyütme, derin öğrenme mimarileri.

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