bibtext: @InProceedings{ Liu_Controllable_MICCAI2024,
                   author = { Liu, Shiyu and Wang, Fan and Ren, Zehua and Lian, Chunfeng and Ma, Jianhua },
                   title = { { Controllable Counterfactual Generation for Interpretable Medical Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Counterfactual generation is used to solve the problem of lack of interpreta-bility and insufficient data in deep diagnostic models. By synthesize counter-factual images based on an image-to-image generation model trained with unpaired data, we can interpret the output of a classification model according to a hypothetical class and enhance the training dataset. Recent counterfactu-al generation approaches based on autoencoders or generative adversarial models are difficult to train or produce realistic images due to the trade-off between image similarity and class difference. In this paper, we propose a new counterfactual generation method based on diffusion models. Our method combines the class-condition control from classifier-free guidance and the reference-image control with attention injection to transform the in-put images with unknown labels into a hypothesis class. Our methods can flexibly adjust the generation trade-off in the inference stage instead of the training stage, providing controllable visual explanations consistent with medical knowledge for clinicians. We demonstrate the effectiveness of our method on the ADNI structural MRI dataset for Alzheimer’s disease diagno-sis and conditional 3D image2image generation tasks. Our codes can be found at https://github.com/ladderlab-xjtu/ControlCG.
title: Controllable Counterfactual Generation for Interpretable Medical Image Classification
authors: Liu, Shiyu Wang, Fan Ren, Zehua Lian, Chunfeng Ma, Jianhua