Bootstrapping Large Language Models for Radiology Report Generation
Authors: Chang Liu, Yuanhe Tian, Weidong Chen, Yan Song, Yongdong Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two prevailing RRG datasets, namely, IU X-Ray and MIMIC-CXR, demonstrate the superiority of our approach to previous state-of-the-art solutions. |
| Researcher Affiliation | Academia | Chang Liu1, Yuanhe Tian2, Weidong Chen1, Yan Song1 , Yongdong Zhang1 1University of Science and Technology of China 2University of Washington lc980413@mail.ustc.edu.cn, yhtian@uw.edu, chenweidong@ustc.edu.cn, clksong@gmail.com, zhyd73@ustc.edu.cn |
| Pseudocode | No | The paper describes the proposed approach in narrative text and with a system architecture diagram, but it does not include pseudocode or an algorithm block. |
| Open Source Code | Yes | Code and models of our approach are available at https:// github.com/synlp/R2-LLM. |
| Open Datasets | Yes | We conduct our experiments on two conventional benchmark datasets, i.e., IU X-RAY (Demner-Fushman et al. 2016) from Indiana University and MIMIC-CXR (Johnson et al. 2019) from the Beth Israel Deaconess Medical Center. |
| Dataset Splits | Yes | We follow the dataset split in Li et al. (2018) for IU X-RAY and the official split of MIMIC-CXR. Table 1 reports the statistics of all datasets in terms of the numbers of radiographs, reports, patients, and average report length according to each split of the datasets. |
| Hardware Specification | No | The paper specifies components like 'Vi T-G version of vision transformer from EVA-CLIP' and 'Vicuna (13B) as the text generator' but does not provide specific hardware details such as GPU models, CPU types, or memory used for experiments. |
| Software Dependencies | No | The paper mentions 'Mini GPT-4', 'Vicuna (13B)', 'Med CLIP', and 'Adam W' as tools used, but it does not specify version numbers for these software components or other key dependencies like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | The batch sizes for IU X-RAY and MIMIC-CXR are set to 12. The weights to balance I3 and C2FD loss in Eq. (2) are set to β1 = 1 and β2 = 1, respectively. In training, we only update parameters in the linear projection layer in the visual encoder and Vicuna through Adam W (Kingma and Ba 2015) with learning rate set to 1 10 6. |