3 research outputs found
xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network
In the domain of unsupervised image-to-image transformation using generative
transformative models, CycleGAN has become the architecture of choice. One of
the primary downsides of this architecture is its relatively slow rate of
convergence. In this work, we use discriminator-driven explainability to speed
up the convergence rate of the generative model by using saliency maps from the
discriminator that mask the gradients of the generator during backpropagation,
based on the work of Nagisetty et al., and also introducing the saliency map on
input, added onto a Gaussian noise mask, by using an interpretable latent
variable based on Wang M.'s Mask CycleGAN. This allows for an explainability
fusion in both directions, and utilizing the noise-added saliency map on input
as evidence-based counterfactual filtering. This new architecture has much
higher rate of convergence than a baseline CycleGAN architecture while
preserving the image quality.Comment: 10 pages, 4 figures, ICVS TU Wien 202
Dosimetric comparison of inverse optimisation methods versus forward optimisation in HDR brachytherapy of breast, cervical and prostate cancer
Attention-Enhanced Unpaired xAI-GANs for Transformation of Histological Stain Images
Histological staining is the primary method for confirming cancer diagnoses, but certain types, such as p63 staining, can be expensive and potentially damaging to tissues. In our research, we innovate by generating p63-stained images from H&E-stained slides for metaplastic breast cancer. This is a crucial development, considering the high costs and tissue risks associated with direct p63 staining. Our approach employs an advanced CycleGAN architecture, xAI-CycleGAN, enhanced with context-based loss to maintain structural integrity. The inclusion of convolutional attention in our model distinguishes between structural and color details more effectively, thus significantly enhancing the visual quality of the results. This approach shows a marked improvement over the base xAI-CycleGAN and standard CycleGAN models, offering the benefits of a more compact network and faster training even with the inclusion of attention