Modification of DDIM Encoding for Generating Counterfactual Pathology Images of Malignant Lymphoma

Ryoichi Koga, Mauricio Kugler, Tatsuya Yokota, Kouichi Ohshima, Hiroaki Miyoshi, Miharu Nagaishi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani

Abstract: We propose a method that modifies encoding in DDIM (Denoising Diffusion Implicit Model) to improve the quality of counterfactual histopathological images of malignant lymphoma. Counterfactual medical images are widely employed for analyzing the changes in images accompanying disease. For the analysis of pathological images, it is desired to accurately represent the types of individual cells in the tissue. We employ DDIM because it can refer to exogenous variables in causal models and can generate counterfactual images. Here, one problem of DDIM is that it does not always generate accurate images due to approximations in the forward process. In this paper, we propose a method that reduces the errors in the encoded images obtained in the forward process. Since the computation in the backward process of DDIM does not include any approximation, the accurate encoding in the forward process can improve the accuracy of the image generation. Our proposed method improves the accuracy of encod