minor changes to main README (#0)

README.md

@@ -12,10 +12,9 @@ Medical image datasets for AI and ML methods must be diverse (i.e. diagnoses, di
 To address such data scarcity, we proposed generative adversarial networks (GAN)-based models, diffusion-super-resolution-GAN and transformer-based-GAN, to synthesise images of fetal ultrasound brain planes from one public dataset.
 We reported that GAN-based methods can generate 256x256 pixel size of fetal ultrasound trans-cerebellum brain image plane with stable training losses, resulting in lower FID values for diffusion-super-resolution-GAN (average 7.04 and lower FID 5.09 at epoch 10) than the FID values of transformer-based-GAN (average 36.02 and lower 28.93 at epoch 60).
 The results of this work illustrate the potential of GAN-based methods to synthesise realistic high-resolution ultrasound images, leading to future work with other fetal brain planes, anatomies, devices and the need of a pool of experts to evaluate synthesised images.
-Code, data and other resources to reproduce this work are available at \url{https://github.com/budai4medtech/midl2023}.
 
 ![fig](short-paper/figures/main-results/outputs/drawing-v00.png)
-**Figure** Results from Diffusion-Super-resolution-GAN (DSR-GAN) and transformer-based-GAN (TB-GAN):
+**Figure.** Results from Diffusion-Super-resolution-GAN (DSR-GAN) and transformer-based-GAN (TB-GAN):
 	(a) Training losses for Generator and Discriminator networks,
 	(b) FID scores, and
 	(c) 256x256 pixel size trans-cerebellum images of two randomised batches (B1, B2) of real and synthesised (DSR-GAN and TB-GAN).