Radiological Report Generation from Chest X-ray Images Using Pre-trained Word Embeddings

The deep neural networks have facilitated the radiologists to large extent by automating the process of radiological report generation. Majority of the researchers have focussed on improving the learning focus of the model using attention mechanism, reinforcement learning and other techniques. Most of them, have not considered the textual information present in the ground truth radiological reports. In downstream language tasks like text classification, word embedding has played vital role in extracting textual features. Inspired from the same, we empirically study the impact of different word embedding techniques on radiological report generation tasks. In this work, we have used a convolutional neural network and large language model to extract visual and textual features, respectively. Recurrent neural network is used to generate the reports. The proposed method outperforms most of the state-of-the-art methods by achieving following evaluation metrics scores: BLEU-1 = 0.612, BLEU-2 = 0.610, BLEU-3 = 0.608, BLEU-4 = 0.606, ROUGE = 0.811, and CIDEr = 0.317. This work confirms that pre-trained large language model gives significantly better results that other word embedding techniques.