SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering

Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-trained. In the biomedical field, most QA datasets are limited by insufficient training examples and the presence of factoid questions. This study proposes a transfer learning-based sentiment-aware model, named SentiMedQAer, for biomedical QA. The proposed method consists of a learning pipeline that utilizes BioBERT to encode text tokens with contextual and domain-specific embeddings, fine-tunes Text-to-Text Transfer Transformer (T5), and RoBERTa models to integrate sentiment information into the model, and trains an XGBoost classifier to output a confidence score to determine the final answer to the question. We validate SentiMedQAer on PubMedQA, a biomedical QA dataset with reasoning-required yes/no questions. Results show that our method outperforms the SOTA by 15.83% and a single human annotator by 5.91%.

An Encoder-Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images

The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen-Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.

Goal-Driven Visual Question Generation from Radiology Images

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions

BACKGROUND AND OBJECTIVE

Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions.

METHODS

This paper describes the system architecture and an evaluation of the developed end-to-end biomedical QA system named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. It takes natural language questions as input, and outputs both short precise answers and summaries as results. The SemBioNLQA system, dealing with four types of questions, is based on (1) handcrafted lexico-syntactic patterns and a machine learning algorithm for question classification, (2) PubMed search engine and UMLS similarity for document retrieval, (3) the BM25 model, stemmed words and UMLS concepts for passage retrieval, and (4) UMLS metathesaurus, BioPortal synonyms, sentiment analysis and term frequency metric for answer extraction.

RESULTS AND CONCLUSION

Compared with the current state-of-the-art biomedical QA systems, SemBioNLQA, a fully automated system, has the potential to deal with a large amount of question and answer types. SemBioNLQA retrieves quickly users' information needs by returning exact answers (e.g., "yes", "no", a biomedical entity name, etc.) and ideal answers (i.e., paragraph-sized summaries of relevant information) for yes/no, factoid and list questions, whereas it provides only the ideal answers for summary questions. Moreover, experimental evaluations performed on biomedical questions and answers provided by the BioASQ challenge especially in 2015, 2016 and 2017 (as part of our participation), show that SemBioNLQA achieves good performances compared with the most current state-of-the-art systems and allows a practical and competitive alternative to help information seekers find exact and ideal answers to their biomedical questions. The SemBioNLQA source code is publicly available at https://github.com/sarrouti/sembionlqa.