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Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks

arXiv 2405.16119 ↗First posted 25 May 2024cs.LG eess.IVcited by 0

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Abstract (arXiv)

A wide variety of biomedical image data, as well as methods for generating training images using basic deep neural networks, were analyzed. Additionally, all platforms for creating images were analyzed, considering their characteristics. The article develops a method for generating artificial biomedical images based on GAN. GAN architecture has been developed for biomedical image synthesis. The data foundation and module for generating training images were designed and implemented in a software system. A comparison of the generated image database with known databases was made.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 25 May 2024.

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References · 12

  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  3. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  4. 2018Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challengecited by 61
  5. 2017How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosiscited by 35
  6. 2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
  7. 2018Learning Implicit Brain MRI Manifolds with Deep Learningcited by 20
  8. 2018Generating Highly Realistic Images of Skin Lesions with GANscited by 16
  9. 2018MelanoGANs: High Resolution Skin Lesion Synthesis with GANscited by 14
  10. 2017Unsupervised Histopathology Image Synthesiscited by 11
  11. 2021Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesiscited by 10
  12. 2022Unified Framework for Histopathology Image Augmentation and Classification via Generative Modelscited by 3

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