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. 2022 May 24;14(11):2590. doi: 10.3390/cancers14112590

Figure 7.

Figure 7

Overview of the conditional generative adversarial network study design. A conditional generative adversarial network (CGAN) for histology images with molecular labels. (A) Overview of the generator network for generation of synthetic histology image patches with 512 × 512 × 3 pixels. MSI, microsatellite instable; MSS, microsatellite stable; Conv’, transposed convolution 2D layer; BN, batch normalization layer; ReLu, rectified linear unit layer. (B) Overview of the discriminator network for classifying images as real or fake (synthetic). Conv, convolution 2D layer; ReLu*, leaky rectified linear unit layer. (C) Progress of synthetic images from 2000 (2K) to 20,000 (20K) epochs. (D) Final output of the generator network after 50,000 (50K) epochs. Reprinted from Ref. [56].