Authors:
Ryoichi Koga
1
;
Mauricio Kugler
1
;
Tatsuya Yokota
1
;
Kouichi Ohshima
2
;
3
;
Hiroaki Miyoshi
2
;
3
;
Miharu Nagaishi
2
;
Noriaki Hashimoto
4
;
Ichiro Takeuchi
4
;
5
and
Hidekata Hontani
1
Affiliations:
1
Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya-shi, Aichi, 466-8555, Japan
;
2
Kurume University Department of Pathology, 67 Asahi-cho, Kurume-shi, Fukuoka, 830-0011, Japan
;
3
The Japanese Society of Pathology, 1-2-5 Yushima, Bunkyo-ku, Tokyo, 113-0034, Japan
;
4
RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
;
5
Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, 464-8601, Japan
Keyword(s):
Counterfactual Images, Diffusion Models, Pathological Images, Malignant Lymphoma, Causal Inference.
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
ing by explicitly referring to the given original image. Experiments demonstrate that our proposed method accurately reconstructs original images, including microstructures such as cell nuclei, and outperforms the conventional DDIM in several measures of image generation.
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