Influence of the additional second neighbor hopping on the spin response in the t-J model

The influence of the additional second neighbor hopping t' on the spin response of the t-J model in the underdoped and optimally doped regimes is studied within the fermion-spin theory. Although the additional second neighbor hopping t' is systematically accompanied with the reduction of the dynamical spin structure factor and susceptibility, the qualitative behavior of the dynamical spin structure factor and susceptibility of the t-t'-J model is the same as in the case of t-J model. The integrated dynamical spin structure factor spectrum is almost t' independent, and the integrated dynamical spin susceptibility still shows the particularly universal behavior as $I(\omega,T)\propto {\rm arctan}[a_{1}\omega/T+a_{3}(\omega/T)^{3}]$.Comment: 12 pages, Latex, Four figures are included, final published version [accepted for publication in Phys. Rev. B (July 1 issue)

PathologyGAN: Learning deep representations of cancer tissue

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H&E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio