bibtext: @InProceedings{ Luo_Textual_MICCAI2024,
                   author = { Luo, Yuanjiang and Li, Hongxiang and Wu, Xuan and Cao, Meng and Huang, Xiaoshuang and Zhu, Zhihong and Liao, Peixi and Chen, Hu and Zhang, Yi },
                   title = { { Textual Inversion and Self-supervised Refinement for Radiology Report Generation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Existing mainstream approaches follow the encoder-decoder paradigm for generating radiology reports. They focus on improving the network structure of encoders and decoders, which leads to two shortcomings: overlooking the modality gap and ignoring report content constraints. In this paper, we proposed Textual Inversion and Self-supervised Refinement (TISR) to address the above two issues. Specifically, textual inversion can project text and image into the same space by representing images as pseudo words to eliminate the cross-modeling gap. Subsequently, self-supervised refinement refines these pseudo words through contrastive loss computation between images and texts, enhancing the fidelity of generated reports to images. Notably, TISR is orthogonal to most existing methods, plug-and-play. We conduct experiments on two widely-used public datasets and achieve significant improvements on various baselines, which demonstrates the effectiveness and generalization of TISR. The code will be available soon.
title: Textual Inversion and Self-supervised Refinement for Radiology Report Generation
authors: Luo, Yuanjiang Li, Hongxiang Wu, Xuan Cao, Meng Huang, Xiaoshuang Zhu, Zhihong Liao, Peixi Chen, Hu Zhang, Yi