Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation
Authors: Bin Yan, Mingtao Pei2982-2990
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the pre-training model on Radiograph Diagnosis and Reports Generation tasks across four challenging datasets: MIMIC-CXR, IU X-Ray, COV-CTR, and NIH, and achieve state-of-the-art results for all the tasks, which demonstrates the effectiveness of our pre-training model. |
| Researcher Affiliation | Academia | Bin Yan, Mingtao Pei* Beijing Laboratory of Intelligent Information Technology School of Computer Science, Beijing Institute of Technology {bean.yan, peimt}@bit.edu.cn |
| Pseudocode | No | The paper describes its algorithms and models in prose and with mathematical formulas, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We pre-train the Clinical-BERT on MIMIC-CXR (Johnson et al. 2019) dataset... The radiograph reports generation task is conducted on IU X-Ray (Demner-Fushman et al. 2016) and COV-CTR (Li et al. 2020b)... The radiograph diagnosis task is conducted on NIH (Wang et al. 2017). |
| Dataset Splits | Yes | We pre-train the Clinical-BERT on MIMIC-CXR (Johnson et al. 2019) dataset... For a fair comparison, we use the official splitting for training, validation, and testing... We randomly split both datasets into training, validation, and testing in the ratio of 7:1:2. The radiograph diagnosis task is conducted on NIH (Wang et al. 2017) dataset... The official splitting set is adopted in the experiment. |
| Hardware Specification | Yes | All experiments are run on two Nvidia 3090 GPUs. |
| Software Dependencies | No | The paper mentions software components like 'BERT-base', 'Dense Net121', 'Adam W', and 'Jieba', but does not provide specific version numbers for these or any other software libraries or frameworks used. |
| Experiment Setup | Yes | The Adam W (Loshchilov and Hutter 2019) optimizer is adopted with a weight decay of 0.01. Batch size is set as 256 with gradient accumulation (every 4 steps). The learning rate for the backbone and the visual extractor are 1e 4 and 5e 5, respectively. We pre-train the model for 50 epochs. |