Denoising diffusion probabilistic model as a GAN generator for breast cancer histology images segmentation

Abstract It is an immensely challenging task to segment the tissue regions and cells in histological images of breast cancer with precision, but the results of this task are extremely significant for the field of computational pathology as a whole. To address this challenge, the integration of the Denoising Diffusion Probabilistic Model (DDPM) and the Generative Adversarial Network (GAN) has been explored. Specifically, we employ a conditional DDPM as the generator within the GAN framework, alongside a conditional adversarial network serving as the discriminator, to achieve segmentation of breast cancer histology images both at the regional and cellular levels. The forward process of the DDPM is first applied to the image mask. As the noise is added step by step, it is conditioned with the pathological image and estimated by a denoising network. To improve the estimated noise, the estimated noise is again conditioned with the pathological image and fed into the discriminator as part of the training process. As part of the test phase, a noise image conditioned with a pathological image is fed into a denoising model trained taking into consideration each time step, which segments the images into regions and cells in the reverse process. Three datasets were used for the experiments, one at a regional level and two at a cellular level. This method outperforms both GAN and diffusion models, as well as current state-of-the-art methods. Specifically, our method shows notable improvements in terms of Dice and IoU metrics over existing state-of-the-art methods.