AI diagnoses lung disease based on X-rays skoltech SciReports
Table 1 Reported mean performance using word-overlap metrics for two medical radiology datasets and one non-medical for general purpose. Models labelled withstand for the models we implemented and trained with the preprocessed MIMIC-CXR data. Other results are cited from the original papers. BLUE-n denotes the BLEU score that uses up to n-grams. The best performance in each configuration is in bold.
Table 2 The clinical efficacy metrics on the MIMIC-CXR dataset. The best results are highlighted in bold. Models labelled withstand for the models we implemented and trained with the preprocessed MIMIC-CXR data. Other results are cited from the original papers. .The first language model learned to generate a short summary at the beginning of the report, based on the findings from a given medical image to provide the content details.
The second language model, the Generative Pretrained Transformer GPT-3, showed promising results in the medical domain. It successfully continued the extracted texts from the first language model, taking into consideration all the findings provided. As GPT-3 is a rather powerful transformer, it summarizes and provides more details on the findings. Natural language generation metrics suggest that using two language models subsequently provides a notable advantage.
One may notice a gap in the context-related performance as each ground truth image is accompanied by multiple reference captions. The drawback in the CIDEr performance points to a suboptimal suitability of the generated output, whereas the Approach 2 does its best. This is due to the image-relevant n-grams occurring frequently in the respective set of reference sentences. The drawback is in the sampling from the GPT-3 distribution.
We investigated various approaches to automatic generation of X-ray image captioning. We proved that the SAT is a strong baseline, outperforming models with Transformer-based decoders. With the help of GPT-3 pre-trained language model, we managed to improve this baseline. The simple method, where the GPT-3 model finishes the report extracted by the Show-Attend-Tell model, yields significant improvements to the standard text generation scores.
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